101
|
Ahlbrandt J, Lablans M, Glocker K, Stahl-Toyota S, Maier-Hein K, Maier-Hein L, Ückert F. Modern Information Technology for Cancer Research: What's in IT for Me? An Overview of Technologies and Approaches. Oncology 2018; 98:363-369. [PMID: 30439700 DOI: 10.1159/000493638] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 09/10/2018] [Indexed: 11/19/2022]
Abstract
Information technology (IT) can enhance or change many scenarios in cancer research for the better. In this paper, we introduce several examples, starting with clinical data reuse and collaboration including data sharing in research networks. Key challenges are semantic interoperability and data access (including data privacy). We deal with gathering and analyzing genomic information, where cloud computing, uncertainties and reproducibility challenge researchers. Also, new sources for additional phenotypical data are shown in patient-reported outcome and machine learning in imaging. Last, we focus on therapy assistance, introducing tools used in molecular tumor boards and techniques for computer-assisted surgery. We discuss the need for metadata to aggregate and analyze data sets reliably. We conclude with an outlook towards a learning health care system in oncology, which connects bench and bedside by employing modern IT solutions.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Frank Ückert
- German Cancer Research Center, Heidelberg, Germany
| |
Collapse
|
102
|
El Naqa I, Kosorok MR, Jin J, Mierzwa M, Ten Haken RK. Prospects and challenges for clinical decision support in the era of big data. JCO Clin Cancer Inform 2018; 2:CCI.18.00002. [PMID: 30613823 PMCID: PMC6317743 DOI: 10.1200/cci.18.00002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Recently, there has been burgeoning interest in developing more effective and robust clinical decision support systems (CDSSs) for oncology. This has been primarily driven by the demands for more personalized and precise medical practice in oncology in the era of so-called Big Data (BD); an era that promises to harness the power of large-scale data flow to revolutionize cancer treatment. This interest in BD analytics has created new opportunities as well as new unmet challenges. These include: routine aggregation and standardization of clinical data; patient privacy; transformation of current analytical approaches to handle such noisy and heterogeneous data; and expanded use of advanced statistical learning methods based on confluence of modern statistical methods and machine learning algorithms. In this review, we present the current status of CDSSs in oncology, the prospects and current challenges of BD analytics, and the promising role of integrated modern statistics and machine learning algorithms in predicting complex clinical endpoints, individualizing treatment rules, and optimizing dynamic personalized treatment regimens. We discuss issues pertaining to these topics and present application examples from an aggregate of experiences. We also discuss the role of human factors in improving the utilization and acceptance of such enhanced CDSSs and how to mitigate possible sources of human error to achieve optimal performance and wider acceptance.
Collapse
Affiliation(s)
- Issam El Naqa
- Issam El Naqa, Judy Jin, Michelle Mierzwa, and Randall K. Ten Haken, University of Michigan, Ann Arbor, MI; and Michael R. Kosorok, University of North Carolina, Chapel Hill, NC
| | - Michael R. Kosorok
- Issam El Naqa, Judy Jin, Michelle Mierzwa, and Randall K. Ten Haken, University of Michigan, Ann Arbor, MI; and Michael R. Kosorok, University of North Carolina, Chapel Hill, NC
| | - Judy Jin
- Issam El Naqa, Judy Jin, Michelle Mierzwa, and Randall K. Ten Haken, University of Michigan, Ann Arbor, MI; and Michael R. Kosorok, University of North Carolina, Chapel Hill, NC
| | - Michelle Mierzwa
- Issam El Naqa, Judy Jin, Michelle Mierzwa, and Randall K. Ten Haken, University of Michigan, Ann Arbor, MI; and Michael R. Kosorok, University of North Carolina, Chapel Hill, NC
| | - Randall K. Ten Haken
- Issam El Naqa, Judy Jin, Michelle Mierzwa, and Randall K. Ten Haken, University of Michigan, Ann Arbor, MI; and Michael R. Kosorok, University of North Carolina, Chapel Hill, NC
| |
Collapse
|
103
|
Larue RTHM, Klaassen R, Jochems A, Leijenaar RTH, Hulshof MCCM, van Berge Henegouwen MI, Schreurs WMJ, Sosef MN, van Elmpt W, van Laarhoven HWM, Lambin P. Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer. Acta Oncol 2018; 57:1475-1481. [PMID: 30067421 DOI: 10.1080/0284186x.2018.1486039] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Radiomic features retrieved from standard CT-images have shown prognostic power in several tumor sites. In this study, we investigated the prognostic value of pretreatment CT radiomic features to predict overall survival of esophageal cancer patients after chemoradiotherapy. MATERIAL AND METHODS Two datasets of independent centers were analyzed, consisting of esophageal cancer patients treated with concurrent chemotherapy (Carboplatin/Paclitaxel) and 41.4Gy radiotherapy, followed by surgery if feasible. In total, 1049 radiomic features were calculated from the primary tumor volume. Recursive feature elimination was performed to select the 40 most relevant predictors. Using these 40 features and six clinical variables as input, two random forest (RF) models predicting 3-year overall survival were developed. RESULTS In total 165 patients from center 1 and 74 patients from center 2 were used. The radiomics-based RF model yielded an area under the curve (AUC) of 0.69 (95%CI 0.61-0.77), with the top-5 most important features for 3-year survival describing tumor heterogeneity after wavelet filtering. In the validation dataset, the RF model yielded an AUC of 0.61 (95%CI 0.47-0.75). Kaplan Meier plots were significantly different between risk groups in the training dataset (p = .027) and borderline significant in the validation dataset (p = .053). The clinical RF model yielded AUCs of 0.63 (95%CI 0.54-0.71) and 0.62 (95%CI 0.49-0.76) in the training and validation dataset, respectively. Risk groups did not reach a significant correlation with pathological response in the primary tumor. CONCLUSIONS A RF model predicting 3-year overall survival based on pretreatment CT radiomic features was developed and validated in two independent datasets of esophageal cancer patients. The radiomics model had better prognostic power compared to the model using standard clinical variables.
Collapse
Affiliation(s)
- Ruben T. H. M. Larue
- The D-Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Remy Klaassen
- Department of Medical Oncology, Academic Medical Centre, Amsterdam, The Netherlands
| | - Arthur Jochems
- The D-Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Ralph T. H. Leijenaar
- The D-Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | | | - Wendy M. J. Schreurs
- Department of Nuclear Medicine, Zuyderland Medical Centre, Heerlen, The Netherlands
| | - Meindert N. Sosef
- Department of Surgery, Zuyderland Medical Centre, Heerlen, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| |
Collapse
|
104
|
van Wijk Y, Vanneste BGL, Jochems A, Walsh S, Oberije CJ, Pinkawa M, Ramaekers BLT, Vega A, Lambin P. Development of an isotoxic decision support system integrating genetic markers of toxicity for the implantation of a rectum spacer. Acta Oncol 2018; 57:1499-1505. [PMID: 29952681 DOI: 10.1080/0284186x.2018.1484156] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
INTRODUCTION Previous studies revealed that dose escalated radiotherapy for prostate cancer patients leads to higher tumor control probabilities (TCP) but also to higher rectal toxicities. An isotoxic model was developed to maximize the given dose while controlling the toxicity level. This was applied to analyze the effect of an implantable rectum spacer (IRS) and extended with a genetic test of normal tissue radio-sensitivity. A virtual IRS (V-IRS) was tested using this method. We hypothesized that the patients with increased risk of toxicity would benefit more from an IRS. MATERIAL AND METHODS Sixteen localized prostate cancer patients implanted with an IRS were included in the study. Treatment planning was performed on computed tomography (CT) images before and after the placement of the IRS and with a V-IRS. The normal tissue complication probability (NTCP) was calculated using a QUANTEC reviewed model for Grade > =2 late rectal bleeding and the number of fractions of the plans were adjusted until the NTCP value was under 5%. The resulting treatment plans were used to calculate the TCP before and after placement of an IRS. This was extended by adding the effect of two published genetic single nucleotide polymorphisms (SNP's) for late rectal bleeding. RESULTS The median TCP resulting from the optimized plans in patients before the IRS was 75.1% [32.6-90.5%]. With IRS, the median TCP is significantly higher: 98.9% [80.8-99.9%] (p < .01). The difference in TCP between the V-IRS and the real IRS was 1.8% [0.0-18.0%]. Placing an IRS in the patients with SNP's improved the TCP from 49.0% [16.1-80.8%] and 48.9% [16.0-72.8%] to 96.3% [67.0-99.5%] and 90.1% [49.0-99.5%] (p < .01) respectively for either SNP. CONCLUSION This study was a proof-of-concept for an isotoxic model with genetic biomarkers with a V-IRS as a multifactorial decision support system for the decision of a placement of an IRS.
Collapse
Affiliation(s)
- Yvonka van Wijk
- The D‐Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Ben G. L. Vanneste
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Arthur Jochems
- The D‐Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Sean Walsh
- The D‐Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Cary J. Oberije
- The D‐Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Michael Pinkawa
- Department of Radiation Oncology, RWTH Aachen University, Aachen, Germany
- Department of Radiation Oncology, MediClin Robert Janker Klinik, Bonn, Germany
| | - Bram L. T. Ramaekers
- Department of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Ana Vega
- Fundación Pública Galega de Medicina Xenómica-SERGAS, Grupo de Medicina Xenómica-USC, CIBERER, IDIS, Santiago de Compostela, Spain
| | - Philippe Lambin
- The D‐Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| |
Collapse
|
105
|
Resteghini C, Trama A, Borgonovi E, Hosni H, Corrao G, Orlandi E, Calareso G, De Cecco L, Piazza C, Mainardi L, Licitra L. Big Data in Head and Neck Cancer. Curr Treat Options Oncol 2018; 19:62. [DOI: 10.1007/s11864-018-0585-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
|
106
|
Bakr S, Gevaert O, Echegaray S, Ayers K, Zhou M, Shafiq M, Zheng H, Benson JA, Zhang W, Leung ANC, Kadoch M, Hoang CD, Shrager J, Quon A, Rubin DL, Plevritis SK, Napel S. A radiogenomic dataset of non-small cell lung cancer. Sci Data 2018; 5:180202. [PMID: 30325352 PMCID: PMC6190740 DOI: 10.1038/sdata.2018.202] [Citation(s) in RCA: 131] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 07/26/2018] [Indexed: 11/09/2022] Open
Abstract
Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being non-invasive, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available via biopsy. We developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, and segmentation maps of tumors in the CT scans. Imaging data are also paired with results of gene mutation analyses, gene expression microarrays and RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes. This dataset was created to facilitate the discovery of the underlying relationship between tumor molecular and medical image features, as well as the development and evaluation of prognostic medical image biomarkers.
Collapse
Affiliation(s)
- Shaimaa Bakr
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Olivier Gevaert
- Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sebastian Echegaray
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kelsey Ayers
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Mu Zhou
- Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Majid Shafiq
- Department of Medicine, Johns Hopkins University, 733 N Broadway, Baltimore, MD 21205, USA
| | - Hong Zheng
- Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jalen Anthony Benson
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Weiruo Zhang
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ann N C Leung
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michael Kadoch
- Department of Radiology, University of California Davis, Sacramento, CA 95817, USA
| | - Chuong D Hoang
- Thoracic and GI Oncology Branch, National Institutes of Health/National Cancer Institute, MD 20892, USA
| | - Joseph Shrager
- Stanford School of Medicine, Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford, CA 94305, USA.,VA Palo Alto Health Care System, CA 94304, USA
| | - Andrew Quon
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Daniel L Rubin
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sylvia K Plevritis
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sandy Napel
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| |
Collapse
|
107
|
Qin A, Gersten D, Liang J, Liu Q, Grill I, Guerrero T, Stevens C, Yan D. A clinical 3D/4D CBCT-based treatment dose monitoring system. J Appl Clin Med Phys 2018; 19:166-176. [PMID: 30306710 PMCID: PMC6236849 DOI: 10.1002/acm2.12474] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Revised: 05/25/2018] [Accepted: 09/09/2018] [Indexed: 12/25/2022] Open
Abstract
To monitor delivered dose and trigger plan adaptation when deviation becomes unacceptable, a clinical treatment dose (Tx‐Dose) reconstruction system based on three‐dimensional (3D)/four‐dimensional (4D)‐cone beam computed tomograpy (CBCT) images was developed and evaluated on various treatment sites, particularly for lung cancer patient treated by stereotactic body radiation therapy (SBRT). This system integrates with our treatment planning system (TPS), Linacs recording and verification system (R&V), and CBCT imaging system, consisting of three modules: Treatment Schedule Monitoring module (TSM), pseudo‐CT Generating module (PCG), and Treatment Dose Reconstruction/evaluation module (TDR). TSM watches the treatment progress in the R&V system and triggers the PCG module when new CBCT is available. PCG retrieves the CBCTs and performs planning CT to CBCT deformable registration (DIR) to generate pseudo‐CT. The 4D‐CBCT images are taken for target localization and correction in lung cancer patient before treatment. To take full advantage of the valuable information carried by 4D‐CBCT, a novel phase‐matching DIR scheme was developed to generate 4D pseudo‐CT images for 4D dose reconstruction. Finally, TDR module creates TPS scripts to automate Tx‐Dose calculation on the pseudo‐CT images. Both initial quantitative commissioning and patient‐specific qualitative quality assurance of the DIR tool were utilized to ensure the DIR quality. The treatment doses of ten patients (six SBRT‐lung, two head and neck (HN), one breast and one prostate cancer patients) were retrospectively constructed and evaluated. The target registration error (mean ± STD: 1.05 ± 1.13 mm) of the DIR tool is comparable to the interobserver uncertainty (0.88 ± 1.31 mm) evaluated by a publically available lung‐landmarks dataset. For lung SBRT patients, the D99 of the final cumulative Tx‐Dose of GTV is 93.8 ± 5.5% (83.7–100.1%) of the originally planned D99. CTV D99 decreases by 3% and mean ipsilateral parotid dose increases by 11.5% for one of the two HN patients. In conclusion, we have demonstrated the feasibility and effectiveness of a treatment dose verification system in our clinical setting.
Collapse
Affiliation(s)
- An Qin
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - David Gersten
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Jian Liang
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Qiang Liu
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Inga Grill
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Craig Stevens
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Di Yan
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| |
Collapse
|
108
|
Sun W, Jiang M, Dang J, Chang P, Yin FF. Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis. Radiat Oncol 2018; 13:197. [PMID: 30290849 PMCID: PMC6173915 DOI: 10.1186/s13014-018-1140-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 09/24/2018] [Indexed: 01/20/2023] Open
Abstract
Background To investigate the effect of machine learning methods on predicting the Overall Survival (OS) for non-small cell lung cancer based on radiomics features analysis. Methods A total of 339 radiomic features were extracted from the segmented tumor volumes of pretreatment computed tomography (CT) images. These radiomic features quantify the tumor phenotypic characteristics on the medical images using tumor shape and size, the intensity statistics and the textures. The performance of 5 feature selection methods and 8 machine learning methods were investigated for OS prediction. The predicted performance was evaluated with concordance index between predicted and true OS for the non-small cell lung cancer patients. The survival curves were evaluated by the Kaplan-Meier algorithm and compared by the log-rank tests. Results The gradient boosting linear models based on Cox’s partial likelihood method using the concordance index feature selection method obtained the best performance (Concordance Index: 0.68, 95% Confidence Interval: 0.62~ 0.74). Conclusions The preliminary results demonstrated that certain machine learning and radiomics analysis method could predict OS of non-small cell lung cancer accuracy. Electronic supplementary material The online version of this article (10.1186/s13014-018-1140-9) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Wenzheng Sun
- School of Information Science and Engineering, Shandong University, Qingdao, Shandong, 266237, People's Republic of China.,Department of Radiation Oncology, Duke University Cancer Center, Durham, NC, 27710, USA
| | - Mingyan Jiang
- School of Information Science and Engineering, Shandong University, Qingdao, Shandong, 266237, People's Republic of China.
| | - Jun Dang
- Department of Oncology, The First Affiliate Hospital of Chongqing Medical University, Chongqing, 400016, People's Republic of China
| | - Panchun Chang
- School of Electrical and Information Engineering, Qilu Institute of Technology, Jinan, Shandong, 250200, People's Republic of China
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Cancer Center, Durham, NC, 27710, USA
| |
Collapse
|
109
|
van Os HJA, Ramos LA, Hilbert A, van Leeuwen M, van Walderveen MAA, Kruyt ND, Dippel DWJ, Steyerberg EW, van der Schaaf IC, Lingsma HF, Schonewille WJ, Majoie CBLM, Olabarriaga SD, Zwinderman KH, Venema E, Marquering HA, Wermer MJH. Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms. Front Neurol 2018; 9:784. [PMID: 30319525 PMCID: PMC6167479 DOI: 10.3389/fneur.2018.00784] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 08/30/2018] [Indexed: 11/24/2022] Open
Abstract
Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables. Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO patients treated with EVT. We applied the following machine learning algorithms: Random Forests, Support Vector Machine, Neural Network, and Super Learner and compared their predictive value with classic logistic regression models using various variable selection methodologies. Outcome variables were good reperfusion (post-mTICI ≥ 2b) and functional independence (modified Rankin Scale ≤2) at 3 months using (1) only baseline variables and (2) baseline and treatment variables. Area under the ROC-curves (AUC) and difference of mean AUC between the models were assessed. Results: We included 1,383 EVT patients, with good reperfusion in 531 (38%) and functional independence in 525 (38%) patients. Machine learning and logistic regression models all performed poorly in predicting good reperfusion (range mean AUC: 0.53–0.57), and moderately in predicting 3-months functional independence (range mean AUC: 0.77–0.79) using only baseline variables. All models performed well in predicting 3-months functional independence using both baseline and treatment variables (range mean AUC: 0.88–0.91) with a negligible difference of mean AUC (0.01; 95%CI: 0.00–0.01) between best performing machine learning algorithm (Random Forests) and best performing logistic regression model (based on prior knowledge). Conclusion: In patients with LVO machine learning algorithms did not outperform logistic regression models in predicting reperfusion and 3-months functional independence after endovascular treatment. For all models at time of admission radiological outcome was more difficult to predict than clinical outcome.
Collapse
Affiliation(s)
| | - Lucas A Ramos
- Department of Biomedical Engineering and Physics, University of Amsterdam, Amsterdam, Netherlands.,Department of Clinical Epidemiology and Biostatistics, University of Amsterdam, Amsterdam, Netherlands
| | - Adam Hilbert
- Department of Biomedical Engineering and Physics, University of Amsterdam, Amsterdam, Netherlands
| | - Matthijs van Leeuwen
- Leiden Institute for Advanced Computer Sciences, Leiden University, Leiden, Netherlands
| | | | - Nyika D Kruyt
- Department of Neurology, Leiden University Medical Center, Leiden, Netherlands
| | | | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands.,Department of Public Health, Erasmus Medical Center, Rotterdam, Netherlands
| | | | - Hester F Lingsma
- Department of Public Health, Erasmus Medical Center, Rotterdam, Netherlands
| | | | - Charles B L M Majoie
- Department of Radiology and Nuclear Medicine, University of Amsterdam, Amsterdam, Netherlands
| | - Silvia D Olabarriaga
- Department of Clinical Epidemiology and Biostatistics, University of Amsterdam, Amsterdam, Netherlands
| | - Koos H Zwinderman
- Department of Clinical Epidemiology and Biostatistics, University of Amsterdam, Amsterdam, Netherlands
| | - Esmee Venema
- Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands.,Department of Public Health, Erasmus Medical Center, Rotterdam, Netherlands
| | - Henk A Marquering
- Department of Biomedical Engineering and Physics, University of Amsterdam, Amsterdam, Netherlands
| | - Marieke J H Wermer
- Department of Neurology, Leiden University Medical Center, Leiden, Netherlands
| | | |
Collapse
|
110
|
Krafft SP, Rao A, Stingo F, Briere TM, Court LE, Liao Z, Martel MK. The utility of quantitative CT radiomics features for improved prediction of radiation pneumonitis. Med Phys 2018; 45:5317-5324. [PMID: 30133809 DOI: 10.1002/mp.13150] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 07/05/2018] [Accepted: 07/23/2018] [Indexed: 12/25/2022] Open
Abstract
PURPOSE The purpose of this study was to explore gains in predictive model performance for radiation pneumonitis (RP) using pretreatment CT radiomics features extracted from the normal lung volume. METHODS A total of 192 patients treated for nonsmall cell lung cancer with definitive radiotherapy were considered in the current study. In addition to clinical and dosimetric data, CT radiomics features were extracted from the total lung volume defined using the treatment planning scan. A total of 6851 features (15 clinical, 298 total lung and heart dosimetric, and 6538 image features) were gathered and considered candidate predictors for modeling of RP grade ≥3. Models were built with the least absolute shrinkage and selection operator (LASSO) logistic regression and applied to the set of candidate predictors with 50 iterations of tenfold nested cross-validation. RESULTS In the current cohort, 30 of 192 patients (15.6%) presented with RP grade ≥3. Average cross-validated AUC (CV-AUC) using only the clinical and dosimetric parameters was 0.51. CV-AUC was 0.68 when total lung CT radiomics features were added. Analysis with the entire set of available predictors revealed seven different image features selected in at least 40% of the model fits. CONCLUSIONS We have successfully incorporated CT radiomics features into a framework for building predictive RP models via LASSO logistic regression. Addition of normal lung image features produced superior model performance relative to traditional dosimetric and clinical predictors of RP, suggesting that pretreatment CT radiomics features should be considered in the context of RP prediction.
Collapse
Affiliation(s)
- Shane P Krafft
- Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.,The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX, USA
| | - Arvind Rao
- Department of Bioinformatics and Computational Biology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Francesco Stingo
- Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Tina Marie Briere
- Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Zhongxing Liao
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Mary K Martel
- Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| |
Collapse
|
111
|
Mayo CS, Phillips M, McNutt TR, Palta J, Dekker A, Miller RC, Xiao Y, Moran JM, Matuszak MM, Gabriel P, Ayan AS, Prisciandaro J, Thor M, Dixit N, Popple R, Killoran J, Kaleba E, Kantor M, Ruan D, Kapoor R, Kessler ML, Lawrence TS. Treatment data and technical process challenges for practical big data efforts in radiation oncology. Med Phys 2018; 45:e793-e810. [PMID: 30226286 DOI: 10.1002/mp.13114] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Revised: 06/26/2018] [Accepted: 06/26/2018] [Indexed: 12/20/2022] Open
Abstract
The term Big Data has come to encompass a number of concepts and uses within medicine. This paper lays out the relevance and application of large collections of data in the radiation oncology community. We describe the potential importance and uses in clinical practice. The important concepts are then described and how they have been or could be implemented are discussed. Impediments to progress in the collection and use of sufficient quantities of data are also described. Finally, recommendations for how the community can move forward to achieve the potential of big data in radiation oncology are provided.
Collapse
Affiliation(s)
- C S Mayo
- University of Michigan, Ann Arbor, MI, USA
| | - M Phillips
- University of Washington, Seattle, WA, USA
| | - T R McNutt
- Johns Hopkins University, Baltimore, MD, USA
| | - J Palta
- Virginia Commonwealth University, Richmond, VA, USA
| | - A Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | - Y Xiao
- University of Pennsylvania, Philadelphia, PA, USA
| | - J M Moran
- University of Michigan, Ann Arbor, MI, USA
| | | | - P Gabriel
- University of Pennsylvania, Philadelphia, PA, USA
| | - A S Ayan
- Ohio State University, Columbus, OH, USA
| | | | - M Thor
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - N Dixit
- University of California at San Francisco, San Francisco, CA, USA
| | - R Popple
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - E Kaleba
- University of Michigan, Ann Arbor, MI, USA
| | - M Kantor
- MD Anderson Cancer Center, Houston, TX, USA
| | - D Ruan
- University of California at Los Angeles, Los Angeles, CA, USA
| | - R Kapoor
- Johns Hopkins University, Baltimore, MD, USA
| | | | | |
Collapse
|
112
|
Ibragimov B, Toesca D, Chang D, Yuan Y, Koong A, Xing L. Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT. Med Phys 2018; 45:4763-4774. [PMID: 30098025 DOI: 10.1002/mp.13122] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 07/31/2018] [Accepted: 07/31/2018] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Accurate prediction of radiation toxicity of healthy organs-at-risks (OARs) critically determines the radiation therapy (RT) success. The existing dose-volume histogram-based metric may grossly under/overestimate the therapeutic toxicity after 27% in liver RT and 50% in head-and-neck RT. We propose the novel paradigm for toxicity prediction by leveraging the enormous potential of deep learning and go beyond the existing dose/volume histograms. EXPERIMENTAL DESIGN We employed a database of 125 liver stereotactic body RT (SBRT) cases with follow-up data to train deep learning-based toxicity predictor. Convolutional neural networks (CNNs) were applied to discover the consistent patterns in 3D dose plans associated with toxicities. To enhance the predicting power, we first pretrain the CNNs with transfer learning from 3D CT images of 2644 human organs. CNNs were then trained on liver SBRT cases. Furthermore, nondosimetric pretreatment features, such as patients' demographics, underlying liver diseases, liver-directed therapies, were inputted into the fully connected neural network for more comprehensive prediction. The saliency maps of CNNs were used to estimate the toxicity risks associated with irradiation of anatomical regions of specific OARs. In addition, we applied machine learning solutions to map numerical pretreatment features with hepatobiliary toxicity manifestation. RESULTS Among 125 liver SBRT patients, 58 were treated for liver metastases, 36 for hepatocellular carcinoma, 27 for cholangiocarcinoma, and 4 for other histologies. We observed that CNN we able to achieve accurate hepatobiliary toxicity prediction with the AUC of 0.79, whereas combining CNN for 3D dose plan analysis and fully connected neural networks for numerical feature analysis resulted in AUC of 0.85. Deep learning produces almost two times fewer false-positive toxicity predictions in comparison to DVH-based predictions, when the number of false negatives, i.e., missed toxicities, was minimized. The CNN saliency maps automatically estimated the toxicity risks for portal vein (PV) regions. We discovered that irradiation of the proximal portal vein is associated with two times higher toxicity risks (risk score: 0.66) that irradiation of the left portal vein (risk score: 0.31). CONCLUSIONS The framework offers clinically accurate tools for hepatobiliary toxicity prediction and automatic identification of anatomical regions that are critical to spare during SBRT.
Collapse
Affiliation(s)
- Bulat Ibragimov
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Diego Toesca
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Daniel Chang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yixuan Yuan
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
| | - Albert Koong
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| |
Collapse
|
113
|
El Naqa I, Ruan D, Valdes G, Dekker A, McNutt T, Ge Y, Wu QJ, Oh JH, Thor M, Smith W, Rao A, Fuller C, Xiao Y, Manion F, Schipper M, Mayo C, Moran JM, Ten Haken R. Machine learning and modeling: Data, validation, communication challenges. Med Phys 2018; 45:e834-e840. [PMID: 30144098 DOI: 10.1002/mp.12811] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 12/28/2017] [Accepted: 01/22/2018] [Indexed: 11/06/2022] Open
Abstract
With the era of big data, the utilization of machine learning algorithms in radiation oncology is rapidly growing with applications including: treatment response modeling, treatment planning, contouring, organ segmentation, image-guidance, motion tracking, quality assurance, and more. Despite this interest, practical clinical implementation of machine learning as part of the day-to-day clinical operations is still lagging. The aim of this white paper is to further promote progress in this new field of machine learning in radiation oncology by highlighting its untapped advantages and potentials for clinical advancement, while also presenting current challenges and open questions for future research. The targeted audience of this paper includes newcomers as well as practitioners in the field of medical physics/radiation oncology. The paper also provides general recommendations to avoid common pitfalls when applying these powerful data analytic tools to medical physics and radiation oncology problems and suggests some guidelines for transparent and informative reporting of machine learning results.
Collapse
Affiliation(s)
- Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Dan Ruan
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, USA
| | - Gilmer Valdes
- Department of Radiation Oncology, University of California Los San Francisco, San Francisco, CA, USA
| | - Andre Dekker
- GROW-School for Oncology and Developmental Biology, Department of Radiation Oncology (MAASTRO), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Todd McNutt
- Department of Radiation Oncology, John Hopkins University, Baltimore, MD, USA
| | - Yaorong Ge
- Department of Software and Information Systems, University of North Carolina, Charlotte, NC, USA
| | - Q Jackie Wu
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Maria Thor
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Wade Smith
- Department of Radiation Oncology, University of Washington, Seattle, WA, USA
| | - Arvind Rao
- Department of Radiation Oncology, MD Anderson, Houston, TX, USA.,Department of Bioinformatics and Computational Biology, MD Anderson, Houston, TX, USA
| | - Clifton Fuller
- Department of Radiation Oncology, MD Anderson, Houston, TX, USA
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Frank Manion
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Matthew Schipper
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Charles Mayo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Jean M Moran
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Randall Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| |
Collapse
|
114
|
Elhalawani H, Lin TA, Volpe S, Mohamed ASR, White AL, Zafereo J, Wong AJ, Berends JE, AboHashem S, Williams B, Aymard JM, Kanwar A, Perni S, Rock CD, Cooksey L, Campbell S, Yang P, Nguyen K, Ger RB, Cardenas CE, Fave XJ, Sansone C, Piantadosi G, Marrone S, Liu R, Huang C, Yu K, Li T, Yu Y, Zhang Y, Zhu H, Morris JS, Baladandayuthapani V, Shumway JW, Ghosh A, Pöhlmann A, Phoulady HA, Goyal V, Canahuate G, Marai GE, Vock D, Lai SY, Mackin DS, Court LE, Freymann J, Farahani K, Kaplathy-Cramer J, Fuller CD. Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges. Front Oncol 2018; 8:294. [PMID: 30175071 PMCID: PMC6107800 DOI: 10.3389/fonc.2018.00294] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 07/16/2018] [Indexed: 12/13/2022] Open
Abstract
Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the "HPV" challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the "local recurrence" challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings.
Collapse
Affiliation(s)
- Hesham Elhalawani
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Timothy A. Lin
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Baylor College of Medicine, Houston, TX, United States
| | - Stefania Volpe
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Università degli Studi di Milano, Milan, Italy
| | - Abdallah S. R. Mohamed
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Clinical Oncology and Nuclear Medicine, Alexandria University, Alexandria, Egypt
| | - Aubrey L. White
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- McGovern Medical School, University of Texas, Houston, TX, United States
| | - James Zafereo
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- McGovern Medical School, University of Texas, Houston, TX, United States
| | - Andrew J. Wong
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- School of Medicine, The University of Texas Health Science Center San Antonio, San Antonio, TX, United States
| | - Joel E. Berends
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- School of Medicine, The University of Texas Health Science Center San Antonio, San Antonio, TX, United States
| | - Shady AboHashem
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Bowman Williams
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Furman University, Greenville, SC, United States
| | - Jeremy M. Aymard
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Abilene Christian University, Abilene, TX, United States
| | - Aasheesh Kanwar
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Oncology, Oregon Health and Science University, Portland, OR, United States
| | - Subha Perni
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Crosby D. Rock
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Texas Tech University Health Sciences Center El Paso, El Paso, TX, United States
| | - Luke Cooksey
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- University of North Texas Health Science Center, Fort Worth, TX, United States
| | - Shauna Campbell
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, United States
| | - Pei Yang
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Baylor College of Medicine, Houston, TX, United States
| | - Khahn Nguyen
- Colgate University, Hamilton City, CA, United States
| | - Rachel B. Ger
- Graduate School of Biomedical Sciences, MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Physics, Graduate School of Biomedical Sciences, MD Anderson Cancer Center, Houston, TX, United States
| | - Carlos E. Cardenas
- Graduate School of Biomedical Sciences, MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Physics, Graduate School of Biomedical Sciences, MD Anderson Cancer Center, Houston, TX, United States
| | - Xenia J. Fave
- Moores Cancer Center, University of California, La Jolla, San Diego, CA, United States
| | - Carlo Sansone
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università Degli Studi di Napoli Federico II, Naples, Italy
| | - Gabriele Piantadosi
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università Degli Studi di Napoli Federico II, Naples, Italy
| | - Stefano Marrone
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università Degli Studi di Napoli Federico II, Naples, Italy
| | - Rongjie Liu
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Chao Huang
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kaixian Yu
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Tengfei Li
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Yang Yu
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Youyi Zhang
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Hongtu Zhu
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jeffrey S. Morris
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Veerabhadran Baladandayuthapani
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - John W. Shumway
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Alakonanda Ghosh
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Andrei Pöhlmann
- Fraunhofer-Institut für Fabrikbetrieb und Automatisierung (IFF), Magdeburg, Germany
| | - Hady A. Phoulady
- Department of Computer Science, University of Southern Maine, Portland, OR, United States
| | - Vibhas Goyal
- Indian Institute of Technology Hyderabad, Sangareddy, India
| | | | | | - David Vock
- Department of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States
| | - Stephen Y. Lai
- Department of Head and Neck Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Dennis S. Mackin
- Colgate University, Hamilton City, CA, United States
- Department of Radiation Physics, Graduate School of Biomedical Sciences, MD Anderson Cancer Center, Houston, TX, United States
| | - Laurence E. Court
- Colgate University, Hamilton City, CA, United States
- Department of Radiation Physics, Graduate School of Biomedical Sciences, MD Anderson Cancer Center, Houston, TX, United States
| | - John Freymann
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD, United States
| | - Keyvan Farahani
- National Cancer Institute, Rockville, MD, United States
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, MD, United States
| | - Jayashree Kaplathy-Cramer
- Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging, MGH/Harvard Medical School, Boston, MA, United States
| | - Clifton D. Fuller
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Baylor College of Medicine, Houston, TX, United States
- Department of Radiation Physics, Graduate School of Biomedical Sciences, MD Anderson Cancer Center, Houston, TX, United States
| |
Collapse
|
115
|
Nguyen K, Haytmyradov M, Mostafavi H, Patel R, Surucu M, Block A, Harkenrider MM, Roeske JC. Evaluation of Radiomics to Predict the Accuracy of Markerless Motion Tracking of Lung Tumors: A Preliminary Study. Front Oncol 2018; 8:292. [PMID: 30109215 PMCID: PMC6079207 DOI: 10.3389/fonc.2018.00292] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 07/12/2018] [Indexed: 11/13/2022] Open
Abstract
Template-based matching algorithms are currently being considered for markerless motion tracking of lung tumors. These algorithms use tumor templates derived from the planning CT scan, and track the motion of the tumor on single energy fluoroscopic images obtained at the time of treatment. In cases where bone may obstruct the view of the tumor, dual energy fluoroscopy may be used to enhance soft tissue contrast. The goal of this study is to predict which tumors will have a high degree of accuracy for markerless motion tracking based on radiomic features obtained from the planning CT scan, using peak-to-sidelobe ratio (PSR) as a surrogate of tracking accuracy. In this study, CT imaging data of 8 lung cancer patients were obtained and analyzed through the open source IBEX program to generate 2,287 radiomic features. Agglomerative hierarchical clustering was used to narrow down these features into 145 clusters comprised of the highest correlation to PSR. The features among the clusters with the least inter-correlation were then chosen to limit redundancy in the data. The results of this study demonstrated a number of radiomic features that are positively correlated to PSR. The features with the highest degree of correlation included complexity, orientation and range. This approach may be used to determine patients for whom markerless motion tracking would be beneficial.
Collapse
Affiliation(s)
- Kevin Nguyen
- Stritch School of Medicine, Loyola University Chicago, Maywood, IL, United States
| | - Maksat Haytmyradov
- Department of Radiation Oncology, Loyola University Medical Center, Maywood, IL, United States
| | | | - Rakesh Patel
- Department of Radiation Oncology, Loyola University Medical Center, Maywood, IL, United States
| | - Murat Surucu
- Department of Radiation Oncology, Loyola University Medical Center, Maywood, IL, United States
| | - Alec Block
- Department of Radiation Oncology, Loyola University Medical Center, Maywood, IL, United States
| | - Matthew M Harkenrider
- Department of Radiation Oncology, Loyola University Medical Center, Maywood, IL, United States
| | - John C Roeske
- Department of Radiation Oncology, Loyola University Medical Center, Maywood, IL, United States
| |
Collapse
|
116
|
de Jong EEC, van Elmpt W, Rizzo S, Colarieti A, Spitaleri G, Leijenaar RTH, Jochems A, Hendriks LEL, Troost EGC, Reymen B, Dingemans AMC, Lambin P. Applicability of a prognostic CT-based radiomic signature model trained on stage I-III non-small cell lung cancer in stage IV non-small cell lung cancer. Lung Cancer 2018; 124:6-11. [PMID: 30268481 DOI: 10.1016/j.lungcan.2018.07.023] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2018] [Revised: 05/19/2018] [Accepted: 07/17/2018] [Indexed: 01/30/2023]
Abstract
OBJECTIVES Recently it has been shown that radiomic features of computed tomography (CT) have prognostic information in stage I-III non-small cell lung cancer (NSCLC) patients. We aim to validate this prognostic radiomic signature in stage IV adenocarcinoma patients undergoing chemotherapy. MATERIALS AND METHODS Two datasets of chemo-naive stage IV adenocarcinoma patients were investigated, dataset 1: 285 patients with CTs performed in a single center; dataset 2: 223 patients included in a multicenter clinical trial. The main exclusion criteria were EGFR mutation or unknown mutation status and non-delineated primary tumor. Radiomic features were calculated for the primary tumor. The c-index of cox regression was calculated and compared to the signature performance for overall survival (OS). RESULTS In total CT scans from 195 patients were eligible for analysis. Patients having a prognostic index (PI) lower than the signature median (n = 92) had a significantly better OS than patients with a PI higher than the median (n = 103, HR 1.445, 95% CI 1.07-1.95, p = 0.02, c-index 0.576, 95% CI 0.527-0.624). CONCLUSION The radiomic signature, derived from daily practice CT scans, has prognostic value for stage IV NSCLC, however the signature performs less than previously described for stage I-III NSCLC stages. In the future, machine learning techniques can potentially lead to a better prognostic imaging based model for stage IV NSCLC.
Collapse
Affiliation(s)
- Evelyn E C de Jong
- The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Doctor Tanslaan 12, 6229 ET, Maastricht, The Netherlands.
| | - Stefania Rizzo
- Department of Radiology, European Institute of Oncology, Via Ripamonti 435, 20141 Milano, Italy.
| | - Anna Colarieti
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy.
| | - Gianluca Spitaleri
- Department of Thoracic Oncology, European Institute of Oncology, Via Ripamonti 435, 20141 Milano, Italy.
| | - Ralph T H Leijenaar
- The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.
| | - Arthur Jochems
- The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.
| | - Lizza E L Hendriks
- Department of Pulmonary Diseases, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands.
| | - Esther G C Troost
- Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Händelallee 26/Bldg. 130, 01309 Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Händelallee 26/Bldg. 130, 01309 Dresden, Germany; OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Händelallee 26/Bldg. 130, 01309 Dresden, Germany; German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
| | - Bart Reymen
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Doctor Tanslaan 12, 6229 ET, Maastricht, The Netherlands.
| | - Anne-Marie C Dingemans
- Department of Pulmonary Diseases, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands.
| | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.
| |
Collapse
|
117
|
A national survey of AIRO (Italian Association of Radiation Oncology) brachytherapy (Interventional Radiotherapy) study group. J Contemp Brachytherapy 2018; 10:254-259. [PMID: 30038646 PMCID: PMC6052379 DOI: 10.5114/jcb.2018.76981] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 06/12/2018] [Indexed: 12/02/2022] Open
Abstract
Purpose To review brachytherapy resources and to explore current practice patterns in Italy. Material and methods In 2016, on behalf of the Italian Association of Radiation Oncology (AIRO), the Brachytherapy Study Group proposed conducting a survey in order to identify brachytherapy practice patterns. An electronic questionnaire was sent to all radiotherapy centres in Italy, asking for: 1. General information on the Radiation Oncology Centre (affiliation, whether brachytherapy was delivered or not); 2. Brachytherapy equipment and human resources; 3. Brachytherapy procedures; 4. Brachytherapy assessment (number of patients treated annually, treated sites, and different modalities of treatments). Results A total of 66 questionnaires were returned (33.5% of all brachytherapy centers in Italy), out of which 48 (74%) from non-academic hospitals, 6 (10%) from academic hospitals, and 12 (16%) from private institutions. Most centers (84%) had only one brachytherapy machine; 44% did not deliver brachytherapy treatments or delivered less than demanded because of the lack of staff or expertise, need of modernization, or other reasons. The majority of treatments were administered to outpatients for gynecological tumors. Conclusions This survey illustrates the current status of brachytherapy in Italy and should encourage collaboration to develop, implement, and monitor its use when appropriate.
Collapse
|
118
|
Kang J, Rancati T, Lee S, Oh JH, Kerns SL, Scott JG, Schwartz R, Kim S, Rosenstein BS. Machine Learning and Radiogenomics: Lessons Learned and Future Directions. Front Oncol 2018; 8:228. [PMID: 29977864 PMCID: PMC6021505 DOI: 10.3389/fonc.2018.00228] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 06/04/2018] [Indexed: 12/25/2022] Open
Abstract
Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML approaches is radiogenomics, which is the study of the impact of genomic variations on the sensitivity of normal and tumor tissue to radiation. Currently, patients undergoing radiotherapy are treated using uniform dose constraints specific to the tumor and surrounding normal tissues. This is suboptimal in many ways. First, the dose that can be delivered to the target volume may be insufficient for control but is constrained by the surrounding normal tissue, as dose escalation can lead to significant morbidity and rare. Second, two patients with nearly identical dose distributions can have substantially different acute and late toxicities, resulting in lengthy treatment breaks and suboptimal control, or chronic morbidities leading to poor quality of life. Despite significant advances in radiogenomics, the magnitude of the genetic contribution to radiation response far exceeds our current understanding of individual risk variants. In the field of genomics, ML methods are being used to extract harder-to-detect knowledge, but these methods have yet to fully penetrate radiogenomics. Hence, the goal of this publication is to provide an overview of ML as it applies to radiogenomics. We begin with a brief history of radiogenomics and its relationship to precision medicine. We then introduce ML and compare it to statistical hypothesis testing to reflect on shared lessons and to avoid common pitfalls. Current ML approaches to genome-wide association studies are examined. The application of ML specifically to radiogenomics is next presented. We end with important lessons for the proper integration of ML into radiogenomics.
Collapse
Affiliation(s)
- John Kang
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, United States
| | - Tiziana Rancati
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Sangkyu Lee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Sarah L. Kerns
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, United States
| | - Jacob G. Scott
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, United States
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, United States
| | - Russell Schwartz
- Computational Biology Department, Carnegie Mellon School of Computer Science, Pittsburgh, PA, United States
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Seyoung Kim
- Computational Biology Department, Carnegie Mellon School of Computer Science, Pittsburgh, PA, United States
| | - Barry S. Rosenstein
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| |
Collapse
|
119
|
Ma HF, Lv GX, Cai ZF, Zhang DH. Comparison of the prognosis of neoadjuvant chemoradiotherapy treatment with surgery alone in esophageal carcinoma: a meta-analysis. Onco Targets Ther 2018; 11:3441-3447. [PMID: 29942136 PMCID: PMC6005309 DOI: 10.2147/ott.s145063] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Background Resection remains the best treatment for carcinoma of the esophagus in terms of local control, but local recurrence and distant metastasis remain an issue after surgery. Chemo-radiotherapy (CRT) followed by surgery was associated with significantly improved survival benefit, but the effectiveness of neoadjuvant therapy in patients with resectable esophageal carcinoma remains controversial. The aim of this study was to evaluate the effects of neoadjuvant chemoradiotherapy in resectable esophageal carcinoma compared to surgery alone (SA). Methods A search for publications that compared the efficacy of CRT with SA in resectable esophageal carcinoma was conducted. After a rigorous review of the quality, the data were extracted from eligible trials. The major outcomes measures were odds ratios (ORs). The ORs with their corresponding 95% confidence intervals were the principal measure of effects. For the meta-analysis, Revman 5.3 software was used to analyze the combined pooled ORs using fixed- or random-effects models according to the heterogeneity. Results Our findings revealed that, compared with SA, neoadjuvant CRT was associated with improved overall survival (OS) and progression-free survival times, but the 3- and 5-year OS did not show a statistical difference (P≥0.05). The adjuvant chemotherapy group did not show significant improvement on reference rate and metastasis rate compared with the control group. Conclusion CRT does significantly improve progression-free survival and OS in patients with esophageal cancer compared with SA. However, further assessment is still warranted on the role of CRT in future trials with well-selected patients.
Collapse
Affiliation(s)
- Hai-Feng Ma
- Department of Radiotherapy, Dongyang People's Hospital, Dongyang, Zhejiang, China
| | - Guo-Xiao Lv
- Department of Radiotherapy, Dongyang People's Hospital, Dongyang, Zhejiang, China
| | - Zhong-Fang Cai
- Department of Radiotherapy, Dongyang People's Hospital, Dongyang, Zhejiang, China
| | - Da-Hai Zhang
- Department of Radiotherapy, Dongyang People's Hospital, Dongyang, Zhejiang, China
| |
Collapse
|
120
|
Deist TM, Dankers FJWM, Valdes G, Wijsman R, Hsu IC, Oberije C, Lustberg T, van Soest J, Hoebers F, Jochems A, El Naqa I, Wee L, Morin O, Raleigh DR, Bots W, Kaanders JH, Belderbos J, Kwint M, Solberg T, Monshouwer R, Bussink J, Dekker A, Lambin P. Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers. Med Phys 2018; 45:3449-3459. [PMID: 29763967 PMCID: PMC6095141 DOI: 10.1002/mp.12967] [Citation(s) in RCA: 184] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 04/23/2018] [Accepted: 04/26/2018] [Indexed: 12/21/2022] Open
Abstract
Purpose: Machine learning classification algorithms (classifiers) for
prediction of treatment response are becoming more popular in radiotherapy
literature. General Machine learning literature provides evidence in favor
of some classifier families (random forest, support vector machine, gradient
boosting) in terms of classification performance. The purpose of this study
is to compare such classifiers specifically for (chemo)radiotherapy datasets
and to estimate their average discriminative performance for radiation
treatment outcome prediction. Methods: We collected 12 datasets (3496 patients) from prior studies on
post-(chemo)radiotherapy toxicity, survival, or tumor control with clinical,
dosimetric, or blood biomarker features from multiple institutions and for
different tumor sites, that is, (non-)small-cell lung cancer, head and neck
cancer, and meningioma. Six common classification algorithms with built-in
feature selection (decision tree, random forest, neural network, support
vector machine, elastic net logistic regression, Logit-Boost) were applied
on each dataset using the popular open-source R package
caret. The R code and documentation
for the analysis are available online (https://github.com/timodeist/classifier_selection_code). All
classifiers were run on each dataset in a 100-repeated nested fivefold
cross-validation with hyperparameter tuning. Performance metrics (AUC,
calibration slope and intercept, accuracy, Cohen’s kappa, and Brier
score) were computed. We ranked classifiers by AUC to determine which
classifier is likely to also perform well in future studies. We simulated
the benefit for potential investigators to select a certain classifier for a
new dataset based on our study (pre-selection based on
other datasets) or estimating the best classifier for a dataset
(set-specific selection based on information from the
new dataset) compared with uninformed classifier selection (random
selection). Results: Random forest (best in 6/12 datasets) and elastic net logistic
regression (best in 4/12 datasets) showed the overall best discrimination,
but there was no single best classifier across datasets. Both classifiers
had a median AUC rank of 2. Preselection and set-specific
selection yielded a significant average AUC improvement of 0.02 and 0.02
over random selection with an average AUC rank improvement
of 0.42 and 0.66, respectively. Conclusion: Random forest and elastic net logistic regression yield higher
discriminative performance in (chemo)radiotherapy outcome and toxicity
prediction than other studied classifiers. Thus, one of these two
classifiers should be the first choice for investigators when building
classification models or to benchmark one’s own modeling results
against. Our results also show that an informed preselection of classifiers
based on existing datasets can improve discrimination over random
selection.
Collapse
Affiliation(s)
- Timo M Deist
- The D-lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.,Department of Radiation Oncology, GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Frank J W M Dankers
- Department of Radiation Oncology, GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands.,Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Gilmer Valdes
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Robin Wijsman
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - I-Chow Hsu
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Cary Oberije
- Department of Radiation Oncology, GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Tim Lustberg
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Johan van Soest
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Frank Hoebers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Arthur Jochems
- The D-lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.,Department of Radiation Oncology, GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Olivier Morin
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
| | - David R Raleigh
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Wouter Bots
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands.,Institute for Hyperbaric Oxygen (IvHG), Arnhem, The Netherlands
| | - Johannes H Kaanders
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - José Belderbos
- Department of Radiation Oncology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Margriet Kwint
- Department of Radiation Oncology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Timothy Solberg
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
| | - René Monshouwer
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Johan Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Philippe Lambin
- The D-lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| |
Collapse
|
121
|
Luo Y, McShan DL, Matuszak MM, Ray D, Lawrence TS, Jolly S, Kong FM, Ten Haken RK, Naqa IE. A multiobjective Bayesian networks approach for joint prediction of tumor local control and radiation pneumonitis in nonsmall-cell lung cancer (NSCLC) for response-adapted radiotherapy. Med Phys 2018; 45:10.1002/mp.13029. [PMID: 29862533 PMCID: PMC6279602 DOI: 10.1002/mp.13029] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 05/28/2018] [Accepted: 05/28/2018] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Individualization of therapeutic outcomes in NSCLC radiotherapy is likely to be compromised by the lack of proper balance of biophysical factors affecting both tumor local control (LC) and side effects such as radiation pneumonitis (RP), which are likely to be intertwined. Here, we compare the performance of separate and joint outcomes predictions for response-adapted personalized treatment planning. METHODS A total of 118 NSCLC patients treated on prospective protocols with 32 cases of local progression and 20 cases of RP grade 2 or higher (RP2) were studied. Sixty-eight patients with 297 features before and during radiotherapy were used for discovery and 50 patients were reserved for independent testing. A multiobjective Bayesian network (MO-BN) approach was developed to identify important features for joint LC/RP2 prediction using extended Markov blankets as inputs to develop a BN predictive structure. Cross-validation (CV) was used to guide the MO-BN structure learning. Area under the free-response receiver operating characteristic (AU-FROC) curve was used to evaluate joint prediction performance. RESULTS Important features including single nucleotide polymorphisms (SNPs), micro RNAs, pretreatment cytokines, pretreatment PET radiomics together with lung and tumor gEUDs were selected and their biophysical inter-relationships with radiation outcomes (LC and RP2) were identified in a pretreatment MO-BN. The joint LC/RP2 prediction yielded an AU-FROC of 0.80 (95% CI: 0.70-0.86) upon internal CV. This improved to 0.85 (0.75-0.91) with additional two SNPs, changes in one cytokine and two radiomics PET image features through the course of radiotherapy in a during-treatment MO-BN. This MO-BN model outperformed combined single-objective Bayesian networks (SO-BNs) during-treatment [0.78 (0.67-0.84)]. AU-FROC values in the evaluation of the MO-BN and individual SO-BNs on the testing dataset were 0.77 and 0.68 for pretreatment, and 0.79 and 0.71 for during-treatment, respectively. CONCLUSIONS MO-BNs can reveal possible biophysical cross-talks between competing radiotherapy clinical endpoints. The prediction is improved by providing additional during-treatment information. The developed MO-BNs can be an important component of decision support systems for personalized response-adapted radiotherapy.
Collapse
Affiliation(s)
- Yi Luo
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Daniel L. McShan
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Martha M. Matuszak
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Dipankar Ray
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Theodore S. Lawrence
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Shruti Jolly
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Feng-Ming Kong
- Department of Radiation Oncology, Indiana University, Indianapolis, Indiana, 46202 United States
| | - Randall K. Ten Haken
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Issam El Naqa
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| |
Collapse
|
122
|
Leijenaar RTH, Bogowicz M, Jochems A, Hoebers FJP, Wesseling FWR, Huang SH, Chan B, Waldron JN, O'Sullivan B, Rietveld D, Leemans CR, Brakenhoff RH, Riesterer O, Tanadini-Lang S, Guckenberger M, Ikenberg K, Lambin P. Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study. Br J Radiol 2018; 91:20170498. [PMID: 29451412 PMCID: PMC6223271 DOI: 10.1259/bjr.20170498] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 01/17/2018] [Accepted: 02/12/2018] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVES Human papillomavirus (HPV) positive oropharyngeal cancer (oropharyngeal squamous cell carcinoma, OPSCC) is biologically and clinically different from HPV negative OPSCC. Here, we evaluate the use of a radiomic approach to identify the HPV status of OPSCC. METHODS Four independent cohorts, totaling 778 OPSCC patients with HPV determined by p16 were collected. We randomly assigned 80% of all data for model training (N = 628) and 20% for validation (N = 150). On the pre-treatment CT images, 902 radiomic features were calculated from the gross tumor volume. Multivariable modeling was performed using least absolute shrinkage and selection operator. To assess the impact of CT artifacts in predicting HPV (p16), a model was developed on all training data (Mall) and on the artifact-free subset of training data (Mno art). Models were validated on all validation data (Vall), and the subgroups with (Vart) and without (Vno art) artifacts. Kaplan-Meier survival analysis was performed to compare HPV status based on p16 and radiomic model predictions. RESULTS The area under the receiver operator curve for Mall and Mno art ranged between 0.70 and 0.80 and was not significantly different for all validation data sets. There was a consistent and significant split between survival curves with HPV status determined by p16 [p = 0.007; hazard ratio (HR): 0.46], Mall (p = 0.036; HR: 0.55) and Mno art (p = 0.027; HR: 0.49). CONCLUSION This study provides proof of concept that molecular information can be derived from standard medical images and shows potential for radiomics as imaging biomarker of HPV status. Advances in knowledge: Radiomics has the potential to identify clinically relevant molecular phenotypes.
Collapse
Affiliation(s)
- Ralph TH Leijenaar
- The D-Lab: Decision Support for Precision Medicine GROW - School for Oncology and Developmental Biology & MCCC Maastricht University Medical Centre+ Maastricht, Maastricht, Netherlands
| | - Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Arthur Jochems
- The D-Lab: Decision Support for Precision Medicine GROW - School for Oncology and Developmental Biology & MCCC Maastricht University Medical Centre+ Maastricht, Maastricht, Netherlands
| | - Frank JP Hoebers
- Department of Radiation Oncology, GROW- School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Frederik WR Wesseling
- Department of Radiation Oncology, GROW- School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Sophie H Huang
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Ontario, Canada
| | - Biu Chan
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Ontario, Canada
| | - John N Waldron
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Ontario, Canada
| | - Brian O'Sullivan
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Ontario, Canada
| | - Derek Rietveld
- Department of Radiation Oncology, VU University Medical Center, Amsterdam, Netherlands
| | - C Rene Leemans
- Department of Otolaryngology/Head and Neck Surgery, VU University Medical Center, Amsterdam, Netherlands
| | - Ruud H Brakenhoff
- Department of Otolaryngology/Head and Neck Surgery, VU University Medical Center, Amsterdam, Netherlands
| | - Oliver Riesterer
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Kristian Ikenberg
- Institute of Pathology and Molecular Pathology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine GROW - School for Oncology and Developmental Biology & MCCC Maastricht University Medical Centre+ Maastricht, Maastricht, Netherlands
| |
Collapse
|
123
|
How Advances in Imaging Will Affect Precision Radiation Oncology. Int J Radiat Oncol Biol Phys 2018; 101:292-298. [DOI: 10.1016/j.ijrobp.2018.01.047] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 05/11/2017] [Accepted: 01/12/2018] [Indexed: 11/20/2022]
|
124
|
McNutt TR, Benedict SH, Low DA, Moore K, Shpitser I, Jiang W, Lakshminarayanan P, Cheng Z, Han P, Hui X, Nakatsugawa M, Lee J, Moore JA, Robertson SP, Shah V, Taylor R, Quon H, Wong J, DeWeese T. Using Big Data Analytics to Advance Precision Radiation Oncology. Int J Radiat Oncol Biol Phys 2018; 101:285-291. [DOI: 10.1016/j.ijrobp.2018.02.028] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 02/13/2018] [Accepted: 02/20/2018] [Indexed: 11/25/2022]
|
125
|
Sanduleanu S, Woodruff HC, de Jong EE, van Timmeren JE, Jochems A, Dubois L, Lambin P. Tracking tumor biology with radiomics: A systematic review utilizing a radiomics quality score. Radiother Oncol 2018; 127:349-360. [DOI: 10.1016/j.radonc.2018.03.033] [Citation(s) in RCA: 131] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Revised: 03/02/2018] [Accepted: 03/29/2018] [Indexed: 02/07/2023]
|
126
|
Yahya N, Chua XJ, Manan HA, Ismail F. Inclusion of dosimetric data as covariates in toxicity-related radiogenomic studies. Strahlenther Onkol 2018; 194:780-786. [DOI: 10.1007/s00066-018-1303-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2017] [Accepted: 04/06/2018] [Indexed: 12/25/2022]
|
127
|
Hartgerink D, van der Heijden B, De Ruysscher D, Postma A, Ackermans L, Hoeben A, Anten M, Lambin P, Terhaag K, Jochems A, Dekker A, Schoenmaekers J, Hendriks L, Zindler J. Stereotactic Radiosurgery in the Management of Patients With Brain Metastases of Non-Small Cell Lung Cancer: Indications, Decision Tools and Future Directions. Front Oncol 2018; 8:154. [PMID: 29868476 PMCID: PMC5954030 DOI: 10.3389/fonc.2018.00154] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 04/24/2018] [Indexed: 12/18/2022] Open
Abstract
Brain metastases (BM) frequently occur in non-small cell lung cancer (NSCLC) patients. Most patients with BM have a limited life expectancy, measured in months. Selected patients may experience a very long progression-free survival, for example, patients with a targetable driver mutation. Traditionally, whole-brain radiotherapy (WBRT) has been the cornerstone of the treatment, but its indication is a matter of debate. A randomized trial has shown that for patients with a poor prognosis, WBRT does not add quality of life (QoL) nor survival over the best supportive care. In recent decades, stereotactic radiosurgery (SRS) has become an attractive non-invasive treatment for patients with BM. Only the BM is irradiated to an ablative dose, sparing healthy brain tissue. Intracranial recurrence rates decrease when WBRT is administered following SRS or resection but does not improve overall survival and comes at the expense of neurocognitive function and QoL. The downside of SRS compared with WBRT is a risk of radionecrosis (RN) and a higher risk of developing new BM during follow-up. Currently, SRS is an established treatment for patients with a maximum of four BM. Several promising strategies are currently being investigated to further improve the indication and outcome of SRS for patients with BM: the effectivity and safety of SRS in patients with more than four BM, combining SRS with systemic therapy such as targeted agents or immunotherapy, shared decision-making with SRS as a treatment option, and individualized isotoxic dose prescription to mitigate the risk of RN and further enhance local control probability of SRS. This review discusses the current indications of SRS and future directions of treatment for patients with BM of NSCLC with focus on the value of SRS.
Collapse
Affiliation(s)
- Dianne Hartgerink
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Britt van der Heijden
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Dirk De Ruysscher
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
- Proton Therapy Department South-East Netherlands (ZON-PTC), Maastricht, Netherlands
| | - Alida Postma
- Department of Radiology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Linda Ackermans
- Department of Neurosurgery, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Ann Hoeben
- Department of Medical Oncology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Monique Anten
- Department of Neurology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Karin Terhaag
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Arthur Jochems
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
- Proton Therapy Department South-East Netherlands (ZON-PTC), Maastricht, Netherlands
| | - Janna Schoenmaekers
- Department of Pulmonary Diseases, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Lizza Hendriks
- Department of Pulmonary Diseases, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Jaap Zindler
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
- Proton Therapy Department South-East Netherlands (ZON-PTC), Maastricht, Netherlands
| |
Collapse
|
128
|
Liu Z, Wang Y, Liu X, Du Y, Tang Z, Wang K, Wei J, Dong D, Zang Y, Dai J, Jiang T, Tian J. Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas. Neuroimage Clin 2018; 19:271-278. [PMID: 30035021 PMCID: PMC6051495 DOI: 10.1016/j.nicl.2018.04.024] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 04/11/2018] [Accepted: 04/22/2018] [Indexed: 01/08/2023]
Abstract
Purpose To investigate the association between imaging features and low-grade gliomas (LGG) related epilepsy, and to propose a radiomics-based model for the prediction of LGG-associated epilepsy. Methods This retrospective study consecutively enrolled 286 patients with LGGs (194 in the primary cohort and 92 in the validation cohort). T2-weighted MR images (T2WI) were used to characterize risk factors for LGG-related epilepsy: Tumor location features and 3-D imaging features were determined, following which the interactions between these two kinds of features were analyzed. Elastic net was applied to generate a radiomics signature combining key imaging features associated with the LGG-related epilepsy with the primary cohort, and then a nomogram incorporating radiomics signature and clinical characteristics was developed. The radiomics signature and nomogram were validated in the validation cohort. Results A total of 475 features associated with LGG-related epilepsy were obtained for each patient. A radiomics signature with eleven selected features allowed for discriminating patients with epilepsy or not was detected, which performed better than location and 3-D imaging features. The nomogram incorporating radiomics signature and clinical characteristics achieved a high degree of discrimination with area under receiver operating characteristic (ROC) curve (AUC) at 0.8769 in the primary cohort and 0.8152 in the validation cohort. The nomogram also allowed for good calibration in the primary cohort. Conclusion We developed and validated an effective prediction model for LGG-related epilepsy. Our results suggested that radiomics analysis may enable more precise and individualized prediction of LGG-related epilepsy.
Collapse
Affiliation(s)
- Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing 100190, China
| | - Yinyan Wang
- Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China; Beijing Neurosurgical Institute, Capital Medical University, Beijing 100050, China
| | - Xing Liu
- Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China; Beijing Neurosurgical Institute, Capital Medical University, Beijing 100050, China
| | - Yang Du
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing 100190, China
| | - Zhenchao Tang
- School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, Shandong Province 264209, China
| | - Kai Wang
- Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing 100190, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing 100190, China
| | - Yali Zang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing 100190, China
| | - Jianping Dai
- Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Tao Jiang
- Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China; Beijing Neurosurgical Institute, Capital Medical University, Beijing 100050, China.
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100080, China.
| |
Collapse
|
129
|
Nieder C, Mehta MP, Geinitz H, Grosu AL. Prognostic and predictive factors in patients with brain metastases from solid tumors: A review of published nomograms. Crit Rev Oncol Hematol 2018; 126:13-18. [PMID: 29759555 DOI: 10.1016/j.critrevonc.2018.03.018] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 01/19/2018] [Accepted: 03/25/2018] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE To review published nomograms that predict endpoints such as overall survival (OS) or risk of intracranial relapse in patients with brain metastases from solid tumors. METHODS The methods and results of nomogram studies identified by a systematic search were extracted and compared, stratified by endpoint predicted by the respective nomograms. In particular, validation strategies (external/internal), concordance indices (cut-off 0.75) and comparisons to older models were analyzed. RESULTS Six publications reported on prediction of OS. Most of these analyses focused on one particular primary tumor site, e.g., breast cancer or hepatocellular carcinoma, while the largest study included different primary tumor sites. The median number of patients was 244. Three of six studies included external validation cohorts. With few exceptions, concordance indices <0.75 were reported. In all studies reporting this endpoint, the nomogram outperformed older prognostic scores. Two nomograms focused on development of new brain metastases after radiosurgery (one externally validated), one on survival free from salvage whole brain radiotherapy (WBRT) after radiosurgery, and one on neurologic and non-neurologic death in patients receiving radiosurgery after WBRT failure. All concordance indices of these 4 nomograms were <0.70. CONCLUSION Taking into account concordance indices and comparisons to older prognostic models, the most promising, externally validated nomograms are the breast cancer and the non-small cell lung cancer nomogram predicting OS, and the distant brain failure after radiosurgery nomogram. Additional validation studies as well as continuous monitoring of the models' performance appear necessary to ensure their clinical applicability in the present era of rapidly changing treatment paradigms.
Collapse
Affiliation(s)
- Carsten Nieder
- Department of Oncology and Palliative Medicine, Nordland Hospital, 8092 Bodø, Norway; Department of Clinical Medicine, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway.
| | - Minesh P Mehta
- Department of Radiation Oncology, Miami Cancer Institute, Miami, FL, USA
| | - Hans Geinitz
- Department of Radiation Oncology, St. Vincent's Hospital, Linz, Austria
| | - Anca L Grosu
- Department of Radiation Oncology, University Medical Center, Medical Faculty, University of Freiburg, Germany; German Cancer Consortium (DKTK) Partner Site Freiburg, Germany
| |
Collapse
|
130
|
Peeken JC, Bernhofer M, Wiestler B, Goldberg T, Cremers D, Rost B, Wilkens JJ, Combs SE, Nüsslin F. Radiomics in radiooncology - Challenging the medical physicist. Phys Med 2018; 48:27-36. [PMID: 29728226 DOI: 10.1016/j.ejmp.2018.03.012] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 03/07/2018] [Accepted: 03/20/2018] [Indexed: 02/06/2023] Open
Abstract
PURPOSE Noticing the fast growing translation of artificial intelligence (AI) technologies to medical image analysis this paper emphasizes the future role of the medical physicist in this evolving field. Specific challenges are addressed when implementing big data concepts with high-throughput image data processing like radiomics and machine learning in a radiooncology environment to support clinical decisions. METHODS Based on the experience of our interdisciplinary radiomics working group, techniques for processing minable data, extracting radiomics features and associating this information with clinical, physical and biological data for the development of prediction models are described. A special emphasis was placed on the potential clinical significance of such an approach. RESULTS Clinical studies demonstrate the role of radiomics analysis as an additional independent source of information with the potential to influence the radiooncology practice, i.e. to predict patient prognosis, treatment response and underlying genetic changes. Extending the radiomics approach to integrate imaging, clinical, genetic and dosimetric data ('panomics') challenges the medical physicist as member of the radiooncology team. CONCLUSIONS The new field of big data processing in radiooncology offers opportunities to support clinical decisions, to improve predicting treatment outcome and to stimulate fundamental research on radiation response both of tumor and normal tissue. The integration of physical data (e.g. treatment planning, dosimetric, image guidance data) demands an involvement of the medical physicist in the radiomics approach of radiooncology. To cope with this challenge national and international organizations for medical physics should organize more training opportunities in artificial intelligence technologies in radiooncology.
Collapse
Affiliation(s)
- Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
| | - Michael Bernhofer
- Department of Informatics, Technical University of Munich (TUM), Boltzmannstraße 3, 85748 Garching, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
| | | | - Daniel Cremers
- Department of Informatics, Technical University of Munich (TUM), Boltzmannstraße 3, 85748 Garching, Germany
| | - Burkhard Rost
- Department of Informatics, Technical University of Munich (TUM), Boltzmannstraße 3, 85748 Garching, Germany
| | - Jan J Wilkens
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; Institute of Innovative Radiotherapy (iRT), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Ingolstaedter Landstrasse 1, 85764 Neuherberg, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
| | - Fridtjof Nüsslin
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany.
| |
Collapse
|
131
|
Peeken JC, Goldberg T, Knie C, Komboz B, Bernhofer M, Pasa F, Kessel KA, Tafti PD, Rost B, Nüsslin F, Braun AE, Combs SE. Treatment-related features improve machine learning prediction of prognosis in soft tissue sarcoma patients. Strahlenther Onkol 2018; 194:824-834. [PMID: 29557486 DOI: 10.1007/s00066-018-1294-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Accepted: 03/05/2018] [Indexed: 12/01/2022]
Abstract
BACKGROUND AND PURPOSE Current prognostic models for soft tissue sarcoma (STS) patients are solely based on staging information. Treatment-related data have not been included to date. Including such information, however, could help to improve these models. MATERIALS AND METHODS A single-center retrospective cohort of 136 STS patients treated with radiotherapy (RT) was analyzed for patients' characteristics, staging information, and treatment-related data. Therapeutic imaging studies and pathology reports of neoadjuvantly treated patients were analyzed for signs of response. Random forest machine learning-based models were used to predict patients' death and disease progression at 2 years. Pre-treatment and treatment models were compared. RESULTS The prognostic models achieved high performances. Using treatment features improved the overall performance for all three classification types: prediction of death, and of local and systemic progression (area under the receiver operatoring characteristic curve (AUC) of 0.87, 0.88, and 0.84, respectively). Overall, RT-related features, such as the planning target volume and total dose, had preeminent importance for prognostic performance. Therapy response features were selected for prediction of disease progression. CONCLUSIONS A machine learning-based prognostic model combining known prognostic factors with treatment- and response-related information showed high accuracy for individualized risk assessment. This model could be used for adjustments of follow-up procedures.
Collapse
Affiliation(s)
- Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany. .,Partner Site Munich, Deutsches Konsortium für Translationale Krebsforschung (DKTK), Munich, Germany.
| | | | - Christoph Knie
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany
| | - Basil Komboz
- Allianz SE, Königinstraße 28, 80802, Munich, Germany
| | - Michael Bernhofer
- Department for Bioinformatics and Computational Biology, Informatik 12, Technical University of Munich (TUM), Boltzmannstraße 3, 85748, Garching, Germany
| | - Francesco Pasa
- Department of Computer Science, Informatik 9, Technical University of Munich (TUM), Boltzmannstraße 3, 85748, Garching, Germany.,Chair of Biomedical Physics, Department of Physics, Technical University of Munich (TUM), James-Franck-Straße 1, 85748, Garching, Germany
| | - Kerstin A Kessel
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany.,Institute of Innovative Radiotherapy (iRT), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Ingolstaedter Landstraße 1, 85764, Neuherberg, Germany.,Partner Site Munich, Deutsches Konsortium für Translationale Krebsforschung (DKTK), Munich, Germany
| | - Pouya D Tafti
- Allianz SE, Königinstraße 28, 80802, Munich, Germany
| | - Burkhard Rost
- Department for Bioinformatics and Computational Biology, Informatik 12, Technical University of Munich (TUM), Boltzmannstraße 3, 85748, Garching, Germany
| | - Fridtjof Nüsslin
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany
| | | | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany.,Institute of Innovative Radiotherapy (iRT), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Ingolstaedter Landstraße 1, 85764, Neuherberg, Germany.,Partner Site Munich, Deutsches Konsortium für Translationale Krebsforschung (DKTK), Munich, Germany
| |
Collapse
|
132
|
Radiomic Analysis Reveals Prognostic Information in T1-Weighted Baseline Magnetic Resonance Imaging in Patients With Glioblastoma. Invest Radiol 2018; 52:360-366. [PMID: 28079702 DOI: 10.1097/rli.0000000000000349] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
OBJECTIVES The aim of this study was to investigate whether radiomic analysis with random survival forests (RSFs) can predict overall survival from T1-weighted contrast-enhanced baseline magnetic resonance imaging (MRI) scans in a cohort of glioblastoma multiforme (GBM) patients with uniform treatment. MATERIALS AND METHODS This retrospective study was approved by the institutional review board and informed consent was waived. The MRI scans from 66 patients with newly diagnosed GBM from a previous prospective study were analyzed. Tumors were segmented manually on contrast-enhanced 3-dimensional T1-weighted images. Using these segmentations, P = 208 quantitative image features characterizing tumor shape, signal intensity, and texture were calculated in an automated fashion. On this data set, an RSF was trained using 10-fold cross validation to establish a link between image features and overall survival, and the individual risk for each patient was predicted. The mean concordance index was assessed as a measure of prediction accuracy. Association of individual risk with overall survival was assessed using Kaplan-Meier analysis and a univariate proportional hazards model. RESULTS Mean overall survival was 14 months (range, 0.8-85 months). Mean concordance index of the 10-fold cross-validated RSF was 0.67. Kaplan-Meier analysis clearly distinguished 2 patient groups with high and low predicted individual risk (P = 5.5 × 10). Low predicted individual mortality was found to be a favorable prognostic factor for overall survival in a univariate Cox proportional hazards model (hazards ratio, 1.038; 95% confidence interval, 1.015-1.062; P = 0.0059). CONCLUSIONS This study demonstrates that baseline MRI in GBM patients contains prognostic information, which can be accessed by radiomic analysis using RSFs.
Collapse
|
133
|
Carvalho S, Leijenaar RTH, Troost EGC, van Timmeren JE, Oberije C, van Elmpt W, de Geus-Oei LF, Bussink J, Lambin P. 18F-fluorodeoxyglucose positron-emission tomography (FDG-PET)-Radiomics of metastatic lymph nodes and primary tumor in non-small cell lung cancer (NSCLC) - A prospective externally validated study. PLoS One 2018; 13:e0192859. [PMID: 29494598 PMCID: PMC5832210 DOI: 10.1371/journal.pone.0192859] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 01/31/2018] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Lymph node stage prior to treatment is strongly related to disease progression and poor prognosis in non-small cell lung cancer (NSCLC). However, few studies have investigated metabolic imaging features derived from pre-radiotherapy 18F-fluorodeoxyglucose (FDG) positron-emission tomography (PET) of metastatic hilar/mediastinal lymph nodes (LNs). We hypothesized that these would provide complementary prognostic information to FDG-PET descriptors to only the primary tumor (tumor). METHODS Two independent cohorts of 262 and 50 node-positive NSCLC patients were used for model development and validation. Image features (i.e. Radiomics) including shape and size, first order statistics, texture, and intensity-volume histograms (IVH) (http://www.radiomics.io/) were evaluated by univariable Cox regression on the development cohort. Prognostic modeling was conducted with a 10-fold cross-validated least absolute shrinkage and selection operator (LASSO), automatically selecting amongst FDG-PET-Radiomics descriptors from (1) tumor, (2) LNs or (3) both structures. Performance was assessed with the concordance-index. Development data are publicly available at www.cancerdata.org and Dryad (doi:10.5061/dryad.752153b). RESULTS Common SUV descriptors (maximum, peak, and mean) were significantly related to overall survival when extracted from LNs, as were LN volume and tumor load (summed tumor and LNs' volumes), though this was not true for either SUV metrics or tumor's volume. Feature selection exclusively from imaging information based on FDG-PET-Radiomics, exhibited performances of (1) 0.53 -external 0.54, when derived from the tumor, (2) 0.62 -external 0.56 from LNs, and (3) 0.62 -external 0.59 from both structures, including at least one feature from each sub-category, except IVH. CONCLUSION Combining imaging information based on FDG-PET-Radiomics features from tumors and LNs is desirable to achieve a higher prognostic discriminative power for NSCLC.
Collapse
Affiliation(s)
- Sara Carvalho
- Department of Radiation Oncology (MAASTRO), GROW–School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC +), Maastricht, the Netherlands
| | - Ralph T. H. Leijenaar
- Department of Radiation Oncology (MAASTRO), GROW–School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC +), Maastricht, the Netherlands
| | - Esther G. C. Troost
- Department of Radiation Oncology (MAASTRO), GROW–School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC +), Maastricht, the Netherlands
- Institute of Radiooncology—OncoRay, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Medical Faculty and University Hospital Carl Gustav Carus of Technische Universität Dresden, Dresden, Germany
- OncoRay, National Centre for Radiation Research in Oncology, Medical Faculty and University Hospital Carl Gustav Carus of Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Janna E. van Timmeren
- Department of Radiation Oncology (MAASTRO), GROW–School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC +), Maastricht, the Netherlands
| | - Cary Oberije
- Department of Radiation Oncology (MAASTRO), GROW–School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC +), Maastricht, the Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW–School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC +), Maastricht, the Netherlands
| | - Lioe-Fee de Geus-Oei
- Department of Radiology and Nuclear Medicine, Radboud UMC, Nijmegen, the Netherlands
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
- Biomedical Photonic Imaging Group, MIRA Institute, University of Twente, Enschede, the Netherlands
| | - Johan Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW–School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC +), Maastricht, the Netherlands
- * E-mail:
| |
Collapse
|
134
|
Shirato H, Le QT, Kobashi K, Prayongrat A, Takao S, Shimizu S, Giaccia A, Xing L, Umegaki K. Selection of external beam radiotherapy approaches for precise and accurate cancer treatment. JOURNAL OF RADIATION RESEARCH 2018; 59:i2-i10. [PMID: 29373709 PMCID: PMC5868193 DOI: 10.1093/jrr/rrx092] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Indexed: 05/05/2023]
Abstract
Physically precise external-beam radiotherapy (EBRT) technologies may not translate to the best outcome in individual patients. On the other hand, clinical considerations alone are often insufficient to guide the selection of a specific EBRT approach in patients. We examine the ways in which to compare different EBRT approaches based on physical, biological and clinical considerations, and how they can be enhanced with the addition of biophysical models and machine-learning strategies. The process of selecting an EBRT modality is expected to improve in tandem with knowledge-based treatment planning.
Collapse
Affiliation(s)
- Hiroki Shirato
- Department of Radiation Medicine, Faculty of Medicine, Hokkaido University, North-15 West-7, Kita-ku, 0608638, Sapporo, Hokkaido, Japan
- Global Station for Quantum Medical Science and Engineering, Global Institute for Cooperative Research and Education, Hokkaido University, North-15 West-7, Kita-ku, 0608638, Sapporo, Hokkaido, Japan
- Corresponding author. Department of Radiation Medicine, Faculty of Medicine, Hokkaido University, North-15 West-7, Kita-ku, 0608638, Sapporo, Hokkaido, Japan. Tel: +81-11-706-5977; Fax: +81-11-706-7876;
| | - Quynh-Thu Le
- Global Station for Quantum Medical Science and Engineering, Global Institute for Cooperative Research and Education, Hokkaido University, North-15 West-7, Kita-ku, 0608638, Sapporo, Hokkaido, Japan
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Keiji Kobashi
- Department of Medical Physics, Hokkaido University Hospital, Sapporo, Japan
| | - Anussara Prayongrat
- Department of Radiation Medicine, Faculty of Medicine, Hokkaido University, North-15 West-7, Kita-ku, 0608638, Sapporo, Hokkaido, Japan
| | - Seishin Takao
- Global Station for Quantum Medical Science and Engineering, Global Institute for Cooperative Research and Education, Hokkaido University, North-15 West-7, Kita-ku, 0608638, Sapporo, Hokkaido, Japan
- Department of Medical Physics, Hokkaido University Hospital, Sapporo, Japan
| | - Shinichi Shimizu
- Department of Radiation Medicine, Faculty of Medicine, Hokkaido University, North-15 West-7, Kita-ku, 0608638, Sapporo, Hokkaido, Japan
- Global Station for Quantum Medical Science and Engineering, Global Institute for Cooperative Research and Education, Hokkaido University, North-15 West-7, Kita-ku, 0608638, Sapporo, Hokkaido, Japan
| | - Amato Giaccia
- Global Station for Quantum Medical Science and Engineering, Global Institute for Cooperative Research and Education, Hokkaido University, North-15 West-7, Kita-ku, 0608638, Sapporo, Hokkaido, Japan
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Lei Xing
- Global Station for Quantum Medical Science and Engineering, Global Institute for Cooperative Research and Education, Hokkaido University, North-15 West-7, Kita-ku, 0608638, Sapporo, Hokkaido, Japan
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kikuo Umegaki
- Global Station for Quantum Medical Science and Engineering, Global Institute for Cooperative Research and Education, Hokkaido University, North-15 West-7, Kita-ku, 0608638, Sapporo, Hokkaido, Japan
- Department of Medical Physics, Hokkaido University Hospital, Sapporo, Japan
| |
Collapse
|
135
|
Quantitative assessment of Zirconium-89 labeled cetuximab using PET/CT imaging in patients with advanced head and neck cancer: a theragnostic approach. Oncotarget 2018; 8:3870-3880. [PMID: 27965472 PMCID: PMC5354801 DOI: 10.18632/oncotarget.13910] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 12/02/2016] [Indexed: 12/25/2022] Open
Abstract
Biomarkers predicting treatment response to the monoclonal antibody cetuximab in locally advanced head and neck squamous cell carcinomas (LAHNSCC) are lacking. We hypothesize that tumor accessibility is an important factor in treatment success of the EGFR targeting drug. We quantified uptake of cetuximab labeled with Zirconium-89 (89Zr) using PET/CT imaging. Seventeen patients with stage III-IV LAHNSCC received a loading dose unlabeled cetuximab, followed by 10 mg 54.5±9.6 MBq 89Zr-cetuximab. PET/CT images were acquired either 3 and 6 or 4 and 7 days post-injection. 89Zr-cetuximab uptake was quantified using standardized uptake value (SUV) and tumor-to-background ratio (TBR), and correlated to EGFR immunohistochemistry. TBR was compared between scan days to determine optimal timing. Uptake of 89Zr-cetuximab varied between patients (day 6-7: SUVpeak range 2.5-6.2). TBR increased significantly (49±28%, p < 0.01) between first (1.1±0.3) and second scan (1.7±0.6). Between groups with a low and high EGFR expression a significant difference in SUVmean (2.1 versus 3.0) and SUVpeak (3.2 versus 4.7) was found, however, not in TBR. Data is available at www.cancerdata.org (DOI: 10.17195/candat.2016.11.1). In conclusion, 89Zr-cetuximab PET imaging shows large inter-patient variety in LAHNSCC and provides additional information over FDG-PET and EGFR expression. Validation of the predictive value is recommended with scans acquired 6-7 days post-injection.
Collapse
|
136
|
Les big data , généralités et intégration en radiothérapie. Cancer Radiother 2018; 22:73-84. [DOI: 10.1016/j.canrad.2017.04.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 04/11/2017] [Accepted: 04/19/2017] [Indexed: 12/25/2022]
|
137
|
Jochems A, El-Naqa I, Kessler M, Mayo CS, Jolly S, Matuszak M, Faivre-Finn C, Price G, Holloway L, Vinod S, Field M, Barakat MS, Thwaites D, de Ruysscher D, Dekker A, Lambin P. A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy. Acta Oncol 2018; 57:226-230. [PMID: 29034756 PMCID: PMC6108087 DOI: 10.1080/0284186x.2017.1385842] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Accepted: 09/21/2017] [Indexed: 01/02/2023]
Abstract
BACKGROUND Early death after a treatment can be seen as a therapeutic failure. Accurate prediction of patients at risk for early mortality is crucial to avoid unnecessary harm and reducing costs. The goal of our work is two-fold: first, to evaluate the performance of a previously published model for early death in our cohorts. Second, to develop a prognostic model for early death prediction following radiotherapy. MATERIAL AND METHODS Patients with NSCLC treated with chemoradiotherapy or radiotherapy alone were included in this study. Four different cohorts from different countries were available for this work (N = 1540). The previous model used age, gender, performance status, tumor stage, income deprivation, no previous treatment given (yes/no) and body mass index to make predictions. A random forest model was developed by learning on the Maastro cohort (N = 698). The new model used performance status, age, gender, T and N stage, total tumor volume (cc), total tumor dose (Gy) and chemotherapy timing (none, sequential, concurrent) to make predictions. Death within 4 months of receiving the first radiotherapy fraction was used as the outcome. RESULTS Early death rates ranged from 6 to 11% within the four cohorts. The previous model performed with AUC values ranging from 0.54 to 0.64 on the validation cohorts. Our newly developed model had improved AUC values ranging from 0.62 to 0.71 on the validation cohorts. CONCLUSIONS Using advanced machine learning methods and informative variables, prognostic models for early mortality can be developed. Development of accurate prognostic tools for early mortality is important to inform patients about treatment options and optimize care.
Collapse
Affiliation(s)
- Arthur Jochems
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Issam El-Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Marc Kessler
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Charles S. Mayo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Martha Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Corinne Faivre-Finn
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, Manchester, UK
| | - Gareth Price
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, Manchester, UK
| | - Lois Holloway
- Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
| | - Shalini Vinod
- Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
| | - Matthew Field
- Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
| | | | - David Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia
| | - Dirk de Ruysscher
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| |
Collapse
|
138
|
Johnson C, Price G, Khalifa J, Faivre-Finn C, Dekker A, Moore C, van Herk M. A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications. Radiother Oncol 2018; 126:355-361. [PMID: 29223683 DOI: 10.1016/j.radonc.2017.11.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Revised: 11/14/2017] [Accepted: 11/19/2017] [Indexed: 12/01/2022]
Abstract
BACKGROUND AND PURPOSE The gross tumour volume (GTV) is predictive of clinical outcome and consequently features in many machine-learned models. 4D-planning, however, has prompted substitution of the GTV with the internal gross target volume (iGTV). We present and validate a method to synthesise GTV data from the iGTV, allowing the combination of 3D and 4D planned patient cohorts for modelling. MATERIAL AND METHODS Expert delineations in 40 non-small cell lung cancer patients were used to develop linear fit and erosion methods to synthesise the GTV volume and shape. Quality was assessed using Dice Similarity Coefficients (DSC) and closest point measurements; by calculating dosimetric features; and by assessing the quality of random forest models built on patient populations with and without synthetic GTVs. RESULTS Volume estimates were within the magnitudes of inter-observer delineation variability. Shape comparisons produced mean DSCs of 0.8817 and 0.8584 for upper and lower lobe cases, respectively. A model trained on combined true and synthetic data performed significantly better than models trained on GTV alone, or combined GTV and iGTV data. CONCLUSIONS Accurate synthesis of GTV size from the iGTV permits the combination of lung cancer patient cohorts, facilitating machine learning applications in thoracic radiotherapy.
Collapse
Affiliation(s)
- Corinne Johnson
- Manchester Cancer Research Centre, Division of Molecular and Clinical Cancer Science, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Sciences Centre, UK; The Christie NHS Foundation Trust, Manchester Academic Health Sciences Centre, UK.
| | - Gareth Price
- Manchester Cancer Research Centre, Division of Molecular and Clinical Cancer Science, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Sciences Centre, UK; The Christie NHS Foundation Trust, Manchester Academic Health Sciences Centre, UK
| | - Jonathan Khalifa
- The Christie NHS Foundation Trust, Manchester Academic Health Sciences Centre, UK; Department of Radiation Oncology, Institut Universitaire du Cancer de Toulouse - Oncopole, France
| | - Corinne Faivre-Finn
- Manchester Cancer Research Centre, Division of Molecular and Clinical Cancer Science, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Sciences Centre, UK; The Christie NHS Foundation Trust, Manchester Academic Health Sciences Centre, UK
| | - Andre Dekker
- The MAASTRO Clinic, Maastricht University Medical Centre+, The Netherlands
| | - Christopher Moore
- Manchester Cancer Research Centre, Division of Molecular and Clinical Cancer Science, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Sciences Centre, UK; The Christie NHS Foundation Trust, Manchester Academic Health Sciences Centre, UK
| | - Marcel van Herk
- Manchester Cancer Research Centre, Division of Molecular and Clinical Cancer Science, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Sciences Centre, UK; The Christie NHS Foundation Trust, Manchester Academic Health Sciences Centre, UK
| |
Collapse
|
139
|
Lustberg T, Bailey M, Thwaites DI, Miller A, Carolan M, Holloway L, Rios Velazquez E, Hoebers F, Dekker A. Implementation of a rapid learning platform: Predicting 2-year survival in laryngeal carcinoma patients in a clinical setting. Oncotarget 2018; 7:37288-37296. [PMID: 27095578 PMCID: PMC5095076 DOI: 10.18632/oncotarget.8755] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 03/28/2016] [Indexed: 11/25/2022] Open
Abstract
Background and Purpose To improve quality and personalization of oncology health care, decision aid tools are needed to advise physicians and patients. The aim of this work is to demonstrate the clinical relevance of a survival prediction model as a first step to multi institutional rapid learning and compare this to a clinical trial dataset. Materials and Methods Data extraction and mining tools were used to collect uncurated input parameters from Illawarra Cancer Care Centre's (clinical cohort) oncology information system. Prognosis categories previously established from the Maastricht Radiation Oncology (training cohort) dataset, were applied to the clinical cohort and the radiotherapy only arm of the RTOG-9111 (trial cohort). Results Data mining identified 125 laryngeal carcinoma patients, ending up with 52 patients in the clinical cohort who were eligible to be evaluated by the model to predict 2-year survival and 177 for the trial cohort. The model was able to classify patients and predict survival in the clinical cohort, but for the trial cohort it failed to do so. Conclusions The technical infrastructure and model is able to support the prognosis prediction of laryngeal carcinoma patients in a clinical cohort. The model does not perform well for the highly selective patient population in the trial cohort.
Collapse
Affiliation(s)
- Tim Lustberg
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Michael Bailey
- Illawarra Cancer Care Centre, Illawarra Shoalhaven Local Health District, Wollongong, Australia.,Centre for Oncology Informatics, University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - David I Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia
| | - Alexis Miller
- Illawarra Cancer Care Centre, Illawarra Shoalhaven Local Health District, Wollongong, Australia.,Centre for Oncology Informatics, University of Wollongong, Wollongong, Australia
| | - Martin Carolan
- Illawarra Cancer Care Centre, Illawarra Shoalhaven Local Health District, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Lois Holloway
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia.,South Western Clinical School, University of New South Wales, Sydney, Australia.,Ingham Institute and Liverpool and Macarthur Cancer Therapy Centres, Liverpool, Australia
| | | | - Frank Hoebers
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| |
Collapse
|
140
|
Salem A, Asselin MC, Reymen B, Jackson A, Lambin P, West CML, O'Connor JPB, Faivre-Finn C. Targeting Hypoxia to Improve Non-Small Cell Lung Cancer Outcome. J Natl Cancer Inst 2018; 110:4096546. [PMID: 28922791 DOI: 10.1093/jnci/djx160] [Citation(s) in RCA: 161] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 07/03/2017] [Indexed: 12/18/2022] Open
Abstract
Oxygen deprivation (hypoxia) in non-small cell lung cancer (NSCLC) is an important factor in treatment resistance and poor survival. Hypoxia is an attractive therapeutic target, particularly in the context of radiotherapy, which is delivered to more than half of NSCLC patients. However, NSCLC hypoxia-targeted therapy trials have not yet translated into patient benefit. Recently, early termination of promising evofosfamide and tarloxotinib bromide studies due to futility highlighted the need for a paradigm shift in our approach to avoid disappointments in future trials. Radiotherapy dose painting strategies based on hypoxia imaging require careful refinement prior to clinical investigation. This review will summarize the role of hypoxia, highlight the potential of hypoxia as a therapeutic target, and outline past and ongoing hypoxia-targeted therapy trials in NSCLC. Evidence supporting radiotherapy dose painting based on hypoxia imaging will be critically appraised. Carefully selected hypoxia biomarkers suitable for integration within future NSCLC hypoxia-targeted therapy trials will be examined. Research gaps will be identified to guide future investigation. Although this review will focus on NSCLC hypoxia, more general discussions (eg, obstacles of hypoxia biomarker research and developing a framework for future hypoxia trials) are applicable to other tumor sites.
Collapse
Affiliation(s)
- Ahmed Salem
- Division of Cancer Sciences and Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK; Department of Radiation Oncology (MAASTRO Lab), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Marie-Claude Asselin
- Division of Cancer Sciences and Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK; Department of Radiation Oncology (MAASTRO Lab), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Bart Reymen
- Division of Cancer Sciences and Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK; Department of Radiation Oncology (MAASTRO Lab), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Alan Jackson
- Division of Cancer Sciences and Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK; Department of Radiation Oncology (MAASTRO Lab), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Philippe Lambin
- Division of Cancer Sciences and Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK; Department of Radiation Oncology (MAASTRO Lab), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Catharine M L West
- Division of Cancer Sciences and Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK; Department of Radiation Oncology (MAASTRO Lab), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - James P B O'Connor
- Division of Cancer Sciences and Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK; Department of Radiation Oncology (MAASTRO Lab), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Corinne Faivre-Finn
- Division of Cancer Sciences and Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK; Department of Radiation Oncology (MAASTRO Lab), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| |
Collapse
|
141
|
On the Fuzziness of Machine Learning, Neural Networks, and Artificial Intelligence in Radiation Oncology. Int J Radiat Oncol Biol Phys 2018; 100:1-4. [DOI: 10.1016/j.ijrobp.2017.06.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 06/08/2017] [Indexed: 12/25/2022]
|
142
|
Investigating the Robustness Neighborhood Gray Tone Difference Matrix and Gray Level Co-occurrence Matrix Radiomic Features on Clinical Computed Tomography Systems Using Anthropomorphic Phantoms: Evidence From a Multivendor Study. J Comput Assist Tomogr 2017; 41:995-1001. [PMID: 28708732 DOI: 10.1097/rct.0000000000000632] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE The aim of this study was to determine if optimized imaging protocols across multiple computed tomography (CT) vendors could result in reproducible radiomic features calculated from an anthropomorphic phantom. METHODS Materials with varying degrees of heterogeneity were placed throughout the lungs of the phantom. Twenty scans of the phantom were acquired on 3 CT manufacturers with chest CT protocols that had optimized protocol parameters. Scans were reconstructed using vendor-specific standards and lung kernels. The concordance correlation coefficient (CCC) was used to calculate reproducibility between features. For features with high CCC values, Bland-Altman analysis was also used to quantify agreement. RESULTS The mean Hounsfield unit (HU) was 32.93 HU (141.7 to -26.5 HU) for the rubber insert and 347.2 HU (-320.9 to -347.7 HU) for the wood insert. Low CCC values of less than 0.9 were calculated for all features across all scans. CONCLUSIONS Radiomic features that are derived from the spatial distribution of voxel intensities should be particularly scrutinized for reproducibility in a multivendor environment.
Collapse
|
143
|
Cozzi L, Dinapoli N, Fogliata A, Hsu WC, Reggiori G, Lobefalo F, Kirienko M, Sollini M, Franceschini D, Comito T, Franzese C, Scorsetti M, Wang PM. Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy. BMC Cancer 2017; 17:829. [PMID: 29207975 PMCID: PMC5718116 DOI: 10.1186/s12885-017-3847-7] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 11/27/2017] [Indexed: 12/23/2022] Open
Abstract
Background To appraise the ability of a radiomics based analysis to predict local response and overall survival for patients with hepatocellular carcinoma. Methods A set of 138 consecutive patients (112 males and 26 females, median age 66 years) presented with Barcelona Clinic Liver Cancer (BCLC) stage A to C were retrospectively studied. For a subset of these patients (106) complete information about treatment outcome, namely local control, was available. Radiomic features were computed for the clinical target volume. A total of 35 features were extracted and analyzed. Univariate analysis was used to identify clinical and radiomics significant features. Multivariate models by Cox-regression hazards model were built for local control and survival outcome. Models were evaluated by area under the curve (AUC) of receiver operating characteristic (ROC) curve. For the LC analysis, two models selecting two groups of uncorrelated features were analyzes while one single model was built for the OS analysis. Results The univariate analysis lead to the identification of 15 significant radiomics features but the analysis of cross correlation showed several cross related covariates. The un-correlated variables were used to build two separate models; both resulted into a single significant radiomic covariate: model-1: energy p < 0.05, AUC of ROC 0.6659, C.I.: 0.5585–0.7732; model-2: GLNUp < 0.05, AUC 0.6396, C.I.:0.5266–0.7526. The univariate analysis for covariates significant with respect to local control resulted in 9 clinical and 13 radiomics features with multiple and complex cross-correlations. After elastic net regularization, the most significant covariates were compacity and BCLC stage, with only compacity significant to Cox model fitting (Cox model likelihood ratio test p < 0.0001, compacity p < 0.00001; AUC of the model is 0.8014 (C.I. = 0.7232–0.8797)). Conclusion A robust radiomic signature, made by one single feature was finally identified. A validation phases, based on independent set of patients is scheduled to be performed to confirm the results.
Collapse
Affiliation(s)
- Luca Cozzi
- Radiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, Rozzano, Italy. .,Department of Biomedical Sciences Humanitas University, Rozzano, Italy. .,Humanitas Cancer Center and Research Hospital, Via Manzoni 56, 20089, Rozzano, Milano, Italy.
| | - Nicola Dinapoli
- Polo Scienze Oncologiche ed Ematologiche, Fondazione Policlinico, Universitario Agostino Gemelli, Rome, Italy
| | - Antonella Fogliata
- Radiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, Rozzano, Italy
| | - Wei-Chung Hsu
- Department of Radiation Oncology, Cheng-Ching General Hospital, Taichung, Taiwan
| | - Giacomo Reggiori
- Radiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, Rozzano, Italy
| | - Francesca Lobefalo
- Radiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, Rozzano, Italy
| | - Margarita Kirienko
- Nuclear Medicine Department, Humanitas Clinical and Research Hospital, Rozzano, Italy
| | - Martina Sollini
- Nuclear Medicine Department, Humanitas Clinical and Research Hospital, Rozzano, Italy
| | - Davide Franceschini
- Radiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, Rozzano, Italy
| | - Tiziana Comito
- Radiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, Rozzano, Italy
| | - Ciro Franzese
- Radiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, Rozzano, Italy
| | - Marta Scorsetti
- Radiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, Rozzano, Italy.,Department of Biomedical Sciences Humanitas University, Rozzano, Italy
| | - Po-Ming Wang
- Department of Radiation Oncology, Cheng-Ching General Hospital, Taichung, Taiwan
| |
Collapse
|
144
|
|
145
|
Even AJG, Reymen B, La Fontaine MD, Das M, Mottaghy FM, Belderbos JSA, De Ruysscher D, Lambin P, van Elmpt W. Clustering of multi-parametric functional imaging to identify high-risk subvolumes in non-small cell lung cancer. Radiother Oncol 2017; 125:379-384. [PMID: 29122363 DOI: 10.1016/j.radonc.2017.09.041] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 09/28/2017] [Accepted: 09/28/2017] [Indexed: 01/13/2023]
Abstract
BACKGROUND AND PURPOSE We aimed to identify tumour subregions with characteristic phenotypes based on pre-treatment multi-parametric functional imaging and correlate these subregions to treatment outcome. The subregions were created using imaging of metabolic activity (FDG-PET/CT), hypoxia (HX4-PET/CT) and tumour vasculature (DCE-CT). MATERIALS AND METHODS 36 non-small cell lung cancer (NSCLC) patients underwent functional imaging prior to radical radiotherapy. Kinetic analysis was performed on DCE-CT scans to acquire blood flow (BF) and volume (BV) maps. HX4-PET/CT and DCE-CT scans were non-rigidly co-registered to the planning FDG-PET/CT. Two clustering steps were performed on multi-parametric images: first to segment each tumour into homogeneous subregions (i.e. supervoxels) and second to group the supervoxels of all tumours into phenotypic clusters. Patients were split based on the absolute or relative volume of supervoxels in each cluster; overall survival was compared using a log-rank test. RESULTS Unsupervised clustering of supervoxels yielded four independent clusters. One cluster (high hypoxia, high FDG, intermediate BF/BV) related to a high-risk tumour type: patients assigned to this cluster had significantly worse survival compared to patients not in this cluster (p = 0.035). CONCLUSIONS We designed a subregional analysis for multi-parametric imaging in NSCLC, and showed the potential of subregion classification as a biomarker for prognosis. This methodology allows for a comprehensive data-driven analysis of multi-parametric functional images.
Collapse
Affiliation(s)
- Aniek J G Even
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands; The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology & MCCC, Maastricht University Medical Centre, Maastricht, The Netherlands.
| | - Bart Reymen
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Matthew D La Fontaine
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Marco Das
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, The Netherlands
| | - Felix M Mottaghy
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, The Netherlands; Department of Nuclear Medicine, University Hospital Aachen, Germany
| | - José S A Belderbos
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Dirk De Ruysscher
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands; The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology & MCCC, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| |
Collapse
|
146
|
van Timmeren JE, Leijenaar RTH, van Elmpt W, Reymen B, Lambin P. Feature selection methodology for longitudinal cone-beam CT radiomics. Acta Oncol 2017; 56:1537-1543. [PMID: 28826307 DOI: 10.1080/0284186x.2017.1350285] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND Cone-beam CT (CBCT) scans are typically acquired daily for positioning verification of non-small cell lung cancer (NSCLC) patients. Quantitative information, derived using radiomics, can potentially contribute to (early) treatment adaptation. The aims of this study were to (1) describe and investigate a methodology for feature selection of a longitudinal radiomics approach (2) investigate which time-point during treatment is potentially useful for early treatment response assessment. MATERIAL AND METHODS For 90 NSCLC patients CBCT scans of the first two fractions of treatment (considered as 'test-retest' scans) were analyzed, as well as weekly CBCT images. One hundred and sixteen radiomic features were extracted from the GTV of all scans and subsequently absolute and relative differences were calculated between weekly CBCT images and the CBCT of the first fraction. Test-retest scans were used to determine the smallest detectable change (C = 1.96 * SD) allowing for feature selection by choosing a minimum number of patients for which a feature should change more than 'C' to be considered as relevant. Analysis of which features change at which moment during treatment was used to investigate which time-point is potentially relevant to extract longitudinal radiomics information for early treatment response assessment. RESULTS A total of six absolute delta features changed for at least ten patients at week 2 of treatment and increased to 61 at week 3, 79 at week 4 and 85 at week 5. There was 93% overlap between features selected at week 3 and the other weeks. CONCLUSIONS This study describes a feature selection methodology for longitudinal radiomics that is able to select reproducible delta radiomics features that are informative due to their change during treatment, which can potentially be used for treatment decisions concerning adaptive radiotherapy. Nonetheless, the prognostic value of the selected delta radiomic features should be investigated in future studies.
Collapse
Affiliation(s)
- Janna E. van Timmeren
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| | - Ralph T. H. Leijenaar
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| | - Bart Reymen
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| |
Collapse
|
147
|
Vallières M, Laberge S, Diamant A, El Naqa I. Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept. ACTA ACUST UNITED AC 2017; 62:8536-8565. [DOI: 10.1088/1361-6560/aa8a49] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
|
148
|
Abstract
Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.
Collapse
|
149
|
4DCT imaging to assess radiomics feature stability: An investigation for thoracic cancers. Radiother Oncol 2017; 125:147-153. [DOI: 10.1016/j.radonc.2017.07.023] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 06/12/2017] [Accepted: 07/15/2017] [Indexed: 12/29/2022]
|
150
|
Jochems A, Deist TM, El Naqa I, Kessler M, Mayo C, Reeves J, Jolly S, Matuszak M, Ten Haken R, van Soest J, Oberije C, Faivre-Finn C, Price G, de Ruysscher D, Lambin P, Dekker A. Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries. Int J Radiat Oncol Biol Phys 2017; 99:344-352. [PMID: 28871984 PMCID: PMC5575360 DOI: 10.1016/j.ijrobp.2017.04.021] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 04/13/2017] [Indexed: 12/25/2022]
Abstract
PURPOSE Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. METHODS AND MATERIALS Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. RESULTS Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (P<.001). Learning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). CONCLUSIONS Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe that distributed learning is the future of sharing data in health care.
Collapse
Affiliation(s)
- Arthur Jochems
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.
| | - Timo M Deist
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Marc Kessler
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Chuck Mayo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Jackson Reeves
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Martha Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Randall Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Johan van Soest
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Cary Oberije
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Corinne Faivre-Finn
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, Manchester, UK
| | - Gareth Price
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, Manchester, UK
| | - Dirk de Ruysscher
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| |
Collapse
|